# 1a. Construct the data_MP dataset ---------------------------------------------
data_MP <- EUPeopleIta %>%
# Select the relevant variables (uso LERI_ita_or, non LERI)
dplyr::select(
DIG02_1:DIG02_8, DEM02, DEM02R, VOTEI02_ITA, DEM01,
LERI_ita_or, POLINT, POLINF_1:POLINF_3
) %>%
# Convert variables to factors
dplyr::mutate(across(
c(DIG02_1:DIG02_8, DEM02R, VOTEI02_ITA, DEM01,
LERI_ita_or, POLINT, POLINF_1:POLINF_3),
haven::as_factor
)) %>%
# Order the 1–8 digital use frequency variables
dplyr::mutate(across(
c(DIG02_1:DIG02_8),
~ factor(.x,
levels = c("Never", "Less often", "Several times a month",
"Several times a week", "Once a day",
"Several times a day", "Almost all the time"),
ordered = TRUE)
)) %>%
# Create numeric versions
dplyr::mutate(across(
c(DIG02_1:DIG02_8),
as.integer,
.names = "{.col}_num"
)) %>%
# Rename variables
dplyr::rename(
YouTube_ = DIG02_1,
WhatsApp_ = DIG02_2,
TwitterX_ = DIG02_3,
Instagram_ = DIG02_4,
TikTok_ = DIG02_5,
Facebook_ = DIG02_6,
LinkedIn_ = DIG02_7,
Telegram_ = DIG02_8,
YouTube = DIG02_1_num,
WhatsApp = DIG02_2_num,
Twitter_X = DIG02_3_num,
Instagram = DIG02_4_num,
TikTok = DIG02_5_num,
Facebook = DIG02_6_num,
Linkedin = DIG02_7_num,
Telegram = DIG02_8_num,
Age = DEM02,
Age_cat = DEM02R,
Vote = VOTEI02_ITA,
Sex = DEM01,
Left_Right = LERI_ita_or,
Pol_int = POLINT,
Inf_disagr = POLINF_1,
Inf_check = POLINF_2,
Inf_confirm = POLINF_3
) %>%
# Recode VOTEI02_ITA levels → Other party / Did not vote
dplyr::mutate(
Vote = forcats::fct_recode(
Vote,
"Other party" = "Noi Moderati",
"Other party" = "Impegno Civico-Centro Democratico",
"Other party" = "Italexit per l'Italia",
"Other party" = "Unione Popolare",
"Other party" = "Italia Sovrana e Popolare",
"Other party" = "Vita",
"Other party" = "Altro partito",
"Did not vote" = "Non sono andato a votare",
"Did not vote" = "Ho votato scheda bianca / scheda nulla"
)
)
# ---- vote recode
# Code 20 ("Prefer not to answer") → NA
data_MP <- data_MP %>%
mutate(
Vote_num = as.integer(EUPeopleIta$VOTEI02_ITA),
Vote = case_when(
Vote_num == 20 ~ NA_character_,
TRUE ~ as.character(Vote)
),
Vote = factor(Vote)
) %>%
select(-Vote_num)
# ---- RECODE LERI_ita_or
# # original variable: 1-11 = scale 0-10, 12 = "Not placed", 13 = NA
data_MP <- data_MP %>%
mutate(
Left_Right_num = as.integer(EUPeopleIta$LERI_ita_or),
Left_Right = case_when(
Left_Right_num %in% 1:11 ~ as.character(Left_Right_num - 1),
Left_Right_num == 12 ~ "Not placed",
Left_Right_num == 13 ~ NA_character_,
TRUE ~ NA_character_
),
Left_Right = factor(
Left_Right,
levels = c(as.character(0:10), "Not placed"),
ordered = FALSE
)
) %>%
select(-Left_Right_num)digipol 1 – Italy replication
Setting forlder, dataset and packages
Define data MP_DATASET
Define the USES_DATASET
# 1b. Construct the data_USES dataset ------------------------------------
data_USES <- EUPeopleNet %>%
dplyr::select(dplyr::starts_with("dig03_"))
platforms <- c("YouTube", "WhatsApp", "TwitterX", "Instagram",
"TikTok", "Facebook", "LinkedIn", "Telegram")
platform_prefixes <- paste0("dig03_", 1:8, "_")
purpose_labels <- c(
"friends", "family", "inf_socially", "sup_soc_causes",
"inf_pol", "sup_pol_causes", "connect_profess",
"promote_profess", "other"
)
purpose_suffixes <- 1:9
# Compute binary indicators platform x scope
platform_long <- list()
for (p in seq_along(platforms)) {
plat <- platforms[p]
prefix <- platform_prefixes[p]
for (i in seq_along(purpose_suffixes)) {
pattern_i <- paste0("^", prefix, purpose_suffixes[i], "$")
cols_i <- grep(pattern_i, names(data_USES), value = TRUE)
if (length(cols_i) > 0) {
platform_long[[length(platform_long) + 1]] <- data_USES %>%
mutate(
Platform = plat,
Purpose = purpose_labels[i],
Response = ifelse(
rowSums(across(all_of(cols_i)), na.rm = TRUE) > 0,
"Yes", "No"
)
) %>%
dplyr::select(Platform, Purpose, Response)
}
}
}
data_platform_use <- dplyr::bind_rows(platform_long)
# Summary of "YES" answers
summary_platform_use <- data_platform_use %>%
dplyr::filter(Response == "Yes") %>%
dplyr::group_by(Platform, Purpose) %>%
dplyr::summarise(n = n(), .groups = "drop") %>%
dplyr::group_by(Platform) %>%
dplyr::mutate(Percentage = n / sum(n) * 100) %>%
dplyr::ungroup() %>%
dplyr::mutate(Purpose = factor(Purpose, levels = purpose_labels))
# Graph per platform
#| label: fig_social_media_use_by_purpose
#| fig-width: 9
#| fig-height: 6
#| message: false
#| warning: false
# panels order as ES data
summary_platform_use_plot <- summary_platform_use %>%
dplyr::mutate(
Platform = factor(
Platform,
levels = c("Facebook", "Instagram", "LinkedIn", "Telegram",
"TikTok", "TwitterX", "WhatsApp", "YouTube")
)
)
ggplot(summary_platform_use_plot,
aes(x = Purpose, y = Percentage)) +
geom_col(fill = "darkorange", color = "white", width = 0.75) +
geom_text(
aes(label = sprintf("%.1f%%", Percentage)),
vjust = -0.25,
size = 2.2,
color = "black"
) +
facet_wrap(~ Platform, ncol = 4) +
labs(
title = "Use of Social Media Platform by Purpose",
subtitle = "Percentage of respondents using each platform for each purpose (Yes responses)",
x = "Purpose of Use",
y = "Percentage of Respondents"
) +
scale_y_continuous(
limits = c(0, 50),
breaks = seq(0, 50, 10),
expand = expansion(mult = c(0, 0.08))
) +
theme_minimal(base_size = 11) +
theme(
plot.title = element_text(face = "bold", size = 12),
plot.subtitle = element_text(size = 10),
axis.title.x = element_text(size = 10, margin = margin(t = 7)),
axis.title.y = element_text(size = 10, margin = margin(r = 7)),
axis.text.x = element_text(
angle = 90, hjust = 1, vjust = 0.5, size = 7
),
axis.text.y = element_text(size = 7),
strip.text = element_text(face = "bold", size = 10),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
plot.margin = margin(t = 9, r = 9, b = 9, l = 9)
)
Set platform per purpose
# 1c. ANY PLATFORM PER PURPOSE (Yes / No ) ---------------------
data_any <- EUPeopleNet %>%
dplyr::select(dplyr::starts_with("dig03_"))
labels_use <- c(
"friends", "family", "inf_socially", "sup_soc_causes",
"inf_pol", "sup_pol_causes", "connect_profess",
"promote_profess", "other"
)
purpose_suffixes <- 1:9
for (i in seq_along(labels_use)) {
pattern_i <- paste0("_", purpose_suffixes[i], "$")
cols_i <- grep(pattern_i, names(data_any), value = TRUE)
if (length(cols_i) > 0) {
data_any[[labels_use[i]]] <- ifelse(
rowSums(data_any[, cols_i], na.rm = TRUE) > 0, 1, 0
)
} else {
message("⚠️ No columns found for purpose: ", labels_use[i])
}
}
existing_labels <- intersect(labels_use, names(data_any))
data_any <- data_any %>%
dplyr::mutate(
dplyr::across(
all_of(existing_labels),
~ factor(.x, levels = c(0, 1), labels = c("No", "Yes"))
)
)
summary_uses <- data_any %>%
tidyr::pivot_longer(
cols = all_of(existing_labels),
names_to = "Purpose", values_to = "Response"
) %>%
count(Purpose, Response) %>%
group_by(Purpose) %>%
mutate(Percentage = n / sum(n) * 100) %>%
ungroup()
ggplot(summary_uses, aes(x = Purpose, y = Percentage, fill = Response)) +
geom_col(position = "dodge") +
scale_fill_manual(values = c("grey70", "darkorange")) +
labs(
title = "Use of ANY Social Media Platform by Purpose",
subtitle = "Percentages within each purpose (Yes vs No)",
x = "Purpose",
y = "Percentage of Respondents",
fill = "Response"
) +
theme_minimal(base_size = 10) +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
plot.title = element_text(face = "bold")
)
# 1c-bis. political and sociopolitical purposes indicators --------
data_any <- data_any %>%
mutate(
# political use (any platform)
PolInf_smed = inf_pol,
# political causes (any platform)
PolSup_smed = sup_pol_causes,
# sociopolitical use:
# (social info, social causes support, political information, political causes support)
PolSocial_smed = ifelse(
inf_socially == "Yes" | sup_soc_causes == "Yes" |
inf_pol == "Yes" | sup_pol_causes == "Yes",
"Yes", "No"
),
# political info & support (more strict definition)
PolInfSup_smed = ifelse(
inf_pol == "Yes" | sup_pol_causes == "Yes",
"Yes", "No"
)
) %>%
mutate(
across(
c(PolInf_smed, PolSup_smed, PolSocial_smed, PolInfSup_smed),
~ factor(.x, levels = c("No", "Yes"))
)
)
###Tables
#| label: tables_indicators
#| message: false
#| warning: false
library(dplyr)
library(tidyr)
library(knitr)
# indicators selection
indicators_long <- data_any %>%
dplyr::select(PolInf_smed, PolSup_smed, PolSocial_smed, PolInfSup_smed) %>%
tidyr::pivot_longer(
cols = everything(),
names_to = "Indicator",
values_to = "Value"
) %>%
dplyr::group_by(Indicator, Value) %>%
dplyr::summarise(
n = dplyr::n(),
pct = 100 * n / sum(n),
.groups = "drop"
)
knitr::kable(
indicators_long,
digits = 1,
caption = "Distribution of political/socio-political use (ANY platform)"
)| Indicator | Value | n | pct |
|---|---|---|---|
| PolInfSup_smed | No | 426 | 100 |
| PolInfSup_smed | Yes | 875 | 100 |
| PolInf_smed | No | 441 | 100 |
| PolInf_smed | Yes | 860 | 100 |
| PolSocial_smed | No | 274 | 100 |
| PolSocial_smed | Yes | 1027 | 100 |
| PolSup_smed | No | 607 | 100 |
| PolSup_smed | Yes | 694 | 100 |
### crosstab purposes
#| label: crosstab_PolInf_PolSup
#| message: false
#| warning: false
crosstab_inf_sup <- table(
Information = data_any$PolInf_smed,
Support = data_any$PolSup_smed
)
# Converts in data.frame & adds row percentage
crosstab_df <- as.data.frame.matrix(crosstab_inf_sup)
crosstab_df$Row_Total <- rowSums(crosstab_df)
# Counts table
knitr::kable(
crosstab_df,
caption = "Table: political information vs political causes support (counts)"
)| No | Yes | Row_Total | |
|---|---|---|---|
| No | 426 | 15 | 441 |
| Yes | 181 | 679 | 860 |
# Row percentage
prop_row <- prop.table(crosstab_inf_sup, margin = 1) * 100
prop_row_df <- as.data.frame.matrix(round(prop_row, 1))
knitr::kable(
prop_row_df,
caption = "Table: political information vs political causes support (row percentages)"
)| No | Yes | |
|---|---|---|
| No | 96.6 | 3.4 |
| Yes | 21.0 | 79.0 |
Descriptive plots: platform use and purposes
# 2. Descriptive graphs ======================================
## 2.1a Use frequency per platform (DIG02) – count
data_long <- data_MP %>%
tidyr::pivot_longer(
cols = YouTube_:Telegram_,
names_to = "Platform",
values_to = "Frequency"
)
ggplot(data_long, aes(x = Frequency)) +
geom_bar(fill = "darkorange") +
geom_text(
stat = "count",
aes(label = after_stat(count)), # FIX: after_stat() instead of ..count..
vjust = -0.5,
size = 2
) +
facet_wrap(~ Platform, scales = "free_y", ncol = 4) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(
title = "Social Media Frequency Use (Counts)",
x = NULL,
y = "Count"
) +
scale_y_continuous(expand = expansion(mult = c(0, 0.15)))
## 2.1b Use frequency per platform (DIG02) – percentages
data_long_pct <- data_long %>%
dplyr::group_by(Platform, Frequency) %>%
dplyr::summarise(n = n(), .groups = "drop") %>%
dplyr::group_by(Platform) %>%
dplyr::mutate(pct = 100 * n / sum(n)) %>%
dplyr::ungroup()
ggplot(data_long_pct, aes(x = Frequency, y = pct)) +
geom_col(fill = "darkorange") +
geom_text(
aes(label = sprintf("%.1f%%", pct)),
vjust = -0.3,
size = 2
) +
facet_wrap(~ Platform, scales = "free_y") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(
title = "Social Media Frequency Use (Percentages)",
x = NULL,
y = "Percentages"
) +
scale_y_continuous(expand = expansion(mult = c(0, 0.15)))
## 2.2 Platform use per purpose (from multiple choice var DIG03)
# Count "YES" per platform × purpose
data_long_uses <- data_platform_use %>%
dplyr::filter(Response == "Yes") %>%
dplyr::group_by(Platform, Purpose) %>%
dplyr::summarise(n = n(), .groups = "drop")
ggplot(data_long_uses, aes(x = Purpose, y = n)) +
geom_col(fill = "darkorange") +
geom_text(
aes(label = n),
vjust = -0.3,
size = 2
) +
facet_wrap(~ Platform, scales = "free_y") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(
title = "Uses of Social Media Platforms (Counts)",
x = NULL,
y = "Count"
) +
scale_y_continuous(expand = expansion(mult = c(0, 0.15)))
# Percentage "YES" per platform × purpose
data_long_uses_pct <- data_platform_use %>%
dplyr::filter(Response == "Yes") %>%
dplyr::group_by(Platform, Purpose) %>%
dplyr::summarise(n = n(), .groups = "drop") %>%
dplyr::group_by(Platform) %>%
dplyr::mutate(pct = 100 * n / sum(n)) %>%
dplyr::ungroup()
ggplot(data_long_uses_pct, aes(x = Purpose, y = pct)) +
geom_col(fill = "darkorange") +
geom_text(
aes(label = sprintf("%.1f%%", pct)),
vjust = -0.3,
size = 2
) +
facet_wrap(~ Platform, scales = "free_y") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(
title = "Uses of Social Media Platforms (Percentages)",
x = NULL,
y = "Percentages"
) +
scale_y_continuous(expand = expansion(mult = c(0, 0.15)))
Sociopolitical variables and platform use
## 2.3 Indicators: political and sociopolitical use per platform
# define data_any from EUPeopleNet for safety
data_any <- EUPeopleNet %>%
dplyr::select(dplyr::starts_with("dig03_"))
labels_use <- c(
"friends", "family", "inf_socially", "sup_soc_causes",
"inf_pol", "sup_pol_causes", "connect_profess",
"promote_profess", "other"
)
purpose_suffixes <- 1:9
for (i in seq_along(labels_use)) {
pattern_i <- paste0("_", purpose_suffixes[i], "$")
cols_i <- grep(pattern_i, names(data_any), value = TRUE)
if (length(cols_i) > 0) {
data_any[[labels_use[i]]] <- ifelse(
rowSums(data_any[, cols_i], na.rm = TRUE) > 0, 1, 0
)
}
}
# indexes
data_any <- data_any %>%
mutate(
PolInf_smed = ifelse(inf_pol == 1, 1, 0),
PolSup_smed = ifelse(sup_pol_causes == 1, 1, 0),
PolInfSup_smed = ifelse(inf_pol == 1 | sup_pol_causes == 1, 1, 0),
PolSocial_smed = ifelse(
inf_socially == 1 | sup_soc_causes == 1 |
inf_pol == 1 | sup_pol_causes == 1,
1, 0
)
) %>%
mutate(
across(
c(PolInf_smed, PolSup_smed, PolSocial_smed, PolInfSup_smed),
~ factor(.x, levels = c(0, 1), labels = c("No", "Yes"))
)
)
# single graphs
ggplot(data_any, aes(x = PolInf_smed)) +
geom_bar(fill = "darkgreen") +
theme_minimal() +
labs(
title = "Use of Social Media to Inform Oneself Politically (Any Platform)",
x = NULL,
y = "Count"
)
ggplot(data_any, aes(x = PolSup_smed)) +
geom_bar(fill = "darkgreen") +
theme_minimal() +
labs(
title = "Use of Social Media to Support Political Causes (Any Platform)",
x = NULL,
y = "Count"
)
ggplot(data_any, aes(x = PolSocial_smed)) +
geom_bar(fill = "darkgreen") +
theme_minimal() +
labs(
title = "Use of Social Media for Social/Political Information or Support",
x = NULL,
y = "Count"
)
# Facet with all indicators
data_long_2 <- data_any %>%
tidyr::pivot_longer(
cols = c(PolInf_smed, PolSup_smed, PolSocial_smed, PolInfSup_smed),
names_to = "Indicator",
values_to = "Value"
)
ggplot(data_long_2, aes(x = Value)) +
geom_bar(fill = "darkorange") +
facet_wrap(~ Indicator, scales = "free_y") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(
title = "Political/Social Uses of Social Media (Any Platform)",
x = NULL,
y = "Count"
)
Sociopolitical variables descriptives
## 2.4 sociopolitical descriptives (from data_MP)
# age
ggplot(data_MP, aes(x = Age)) +
geom_histogram(binwidth = 5, fill = "darkorange", color = "white") +
theme_minimal() +
labs(
title = "Age",
x = "How old are you?",
y = "Count"
) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# Gender
ggplot(data_MP, aes(x = Sex)) +
geom_bar(fill = "darkorange") +
theme_minimal() +
labs(
title = "Sex",
x = "Sex",
y = "Count"
) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# Vote in general elections (2022)
ggplot(data_MP %>% filter(!is.na(Vote)), aes(x = Vote)) +
geom_bar(fill = "darkorange") +
theme_minimal() +
labs(
title = "Vote in the Last National Elections (Italy)",
x = "Which party did you vote for in the last national elections?",
y = "Count"
) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# Left-Right ideological placement (plus do not placed)
ggplot(data_MP %>% filter(!is.na(Left_Right)), aes(x = Left_Right)) +
geom_bar(fill = "darkorange") +
theme_minimal() +
labs(
title = "Left–Right self-location",
x = "Left–Right",
y = "Count"
) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# Political interest
ggplot(data_MP, aes(x = Pol_int)) +
geom_bar(fill = "darkorange") +
theme_minimal() +
labs(
title = "Political interest",
x = "Political interest",
y = "Count"
) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# Information disagreement exposure
ggplot(data_MP, aes(x = Inf_disagr)) +
geom_bar(fill = "darkorange") +
theme_minimal() +
labs(
title = "Frequency of exposure to information you disagree with (online)",
x = "How often do you read, watch or listen to something you disagree with?",
y = "Count"
) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
PCA
PCA preparation
# 4. PCA Preparation ----------------------------------------------------
# 4.1 Select variables: 8 supplementary qualitative + 8 quantitative active
data_PCA1 <- data_MP %>%
dplyr::select(
Age_cat, Vote, Sex, Left_Right, Pol_int,
Inf_disagr, Inf_check, Inf_confirm,
YouTube, WhatsApp, Twitter_X, Instagram,
TikTok, Facebook, Linkedin, Telegram
)
# 4.2 Recoding to fit with ES recoding
data_PCA1 <- data_PCA1 %>%
mutate(
Left_Right = recode(
as.character(Left_Right),
"0" = "Ext_left",
"1" = "Ext_left",
"2" = "Left",
"3" = "Left",
"4" = "Center",
"5" = "Center",
"6" = "Center",
"7" = "Right",
"8" = "Right",
"9" = "Ext_right",
"10" = "Ext_right",
"Not placed" = "No_position",
.default = NA_character_
),
Left_Right = factor(
Left_Right,
levels = c("Ext_left", "Left", "Center", "Right", "Ext_right", "No_position")
)
)
# 5. Data Suitability Tests for the PCA ---------------------------------
# 5.0 Correlation matrix of platform variables
cor_matrix <- cor(
data_PCA1[, 9:16],
use = "pairwise.complete.obs"
)
# 5.1 Kaiser–Meyer–Olkin (KMO) Measure
psych::KMO(cor_matrix)Kaiser-Meyer-Olkin factor adequacy
Call: psych::KMO(r = cor_matrix)
Overall MSA = 0.84
MSA for each item =
YouTube WhatsApp Twitter_X Instagram TikTok Facebook Linkedin Telegram
0.89 0.72 0.81 0.83 0.87 0.84 0.84 0.87 # 5.2 Bartlett’s Test of Sphericity (to test whether correlation matrix differs from identity matrix)
psych::cortest.bartlett(cor_matrix, n = nrow(data_PCA1))$chisq
[1] 2351.818
$p.value
[1] 0
$df
[1] 28# 5.3 Multicollinearity and Redundancy (check for variables where r > 0.9)
round(cor_matrix, 2) YouTube WhatsApp Twitter_X Instagram TikTok Facebook Linkedin
YouTube 1.00 0.22 0.33 0.31 0.36 0.20 0.30
WhatsApp 0.22 1.00 0.07 0.36 0.15 0.28 0.11
Twitter_X 0.33 0.07 1.00 0.32 0.45 0.16 0.53
Instagram 0.31 0.36 0.32 1.00 0.40 0.33 0.35
TikTok 0.36 0.15 0.45 0.40 1.00 0.21 0.41
Facebook 0.20 0.28 0.16 0.33 0.21 1.00 0.21
Linkedin 0.30 0.11 0.53 0.35 0.41 0.21 1.00
Telegram 0.33 0.16 0.49 0.40 0.41 0.20 0.44
Telegram
YouTube 0.33
WhatsApp 0.16
Twitter_X 0.49
Instagram 0.40
TikTok 0.41
Facebook 0.20
Linkedin 0.44
Telegram 1.00caret::findCorrelation(cor_matrix, cutoff = 0.9)integer(0)PCA Estimation & visualization
# 6. PCA Estimation and Visualization -----------------------------------
# Here the 8 active variables are platforms,the 8 supplementary variables are: Age_cat, Vote, Sex, Left_Right, Pol_int, Inf_disagr, Inf_check, Inf_confirm
PCA1_result <- FactoMineR::PCA(
data_PCA1,
quali.sup = 1:8,
graph = FALSE
)Warning in FactoMineR::PCA(data_PCA1, quali.sup = 1:8, graph = FALSE): Missing
values are imputed by the mean of the variable: you should use the imputePCA
function of the missMDA package# 6.1 Summary of eigenvalues, contributions, cos², etc.
summary(PCA1_result)
Call:
FactoMineR::PCA(X = data_PCA1, quali.sup = 1:8, graph = FALSE)
Eigenvalues
Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
Variance 3.159 1.207 0.768 0.698 0.620 0.572 0.518
% of var. 39.491 15.090 9.595 8.722 7.749 7.150 6.474
Cumulative % of var. 39.491 54.581 64.176 72.898 80.647 87.797 94.271
Dim.8
Variance 0.458
% of var. 5.729
Cumulative % of var. 100.000
Individuals (the 10 first)
Dist Dim.1 ctr cos2 Dim.2 ctr cos2
1 | 0.000 | 0.000 0.000 0.222 | 0.000 0.000 0.956
2 | 2.944 | 1.380 0.046 0.220 | -0.175 0.002 0.004
3 | 3.457 | 2.479 0.150 0.514 | 1.286 0.105 0.138
4 | 1.917 | -0.564 0.008 0.087 | 0.551 0.019 0.083
5 | 3.821 | 0.725 0.013 0.036 | -0.106 0.001 0.001
6 | 2.831 | -1.359 0.045 0.230 | -0.369 0.009 0.017
7 | 2.084 | -1.008 0.025 0.234 | 0.477 0.014 0.052
8 | 3.095 | 2.772 0.187 0.802 | -0.867 0.048 0.078
9 | 0.537 | -0.138 0.000 0.066 | 0.033 0.000 0.004
10 | 3.723 | 1.833 0.082 0.242 | -1.668 0.177 0.201
Dim.3 ctr cos2
1 | 0.000 0.000 0.002 |
2 | -1.875 0.352 0.406 |
3 | 0.837 0.070 0.059 |
4 | -0.767 0.059 0.160 |
5 | 2.811 0.791 0.541 |
6 | 1.932 0.374 0.466 |
7 | 0.679 0.046 0.106 |
8 | -0.337 0.011 0.012 |
9 | -0.098 0.001 0.033 |
10 | 0.635 0.040 0.029 |
Variables
Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3
YouTube | 0.601 11.419 0.361 | 0.040 0.136 0.002 | 0.581
WhatsApp | 0.389 4.791 0.151 | 0.714 42.224 0.510 | 0.252
Twitter_X | 0.704 15.682 0.495 | -0.406 13.640 0.165 | -0.074
Instagram | 0.688 14.987 0.473 | 0.309 7.914 0.096 | -0.036
TikTok | 0.695 15.276 0.483 | -0.163 2.195 0.026 | 0.093
Facebook | 0.459 6.671 0.211 | 0.523 22.686 0.274 | -0.545
Linkedin | 0.693 15.183 0.480 | -0.308 7.847 0.095 | -0.231
Telegram | 0.711 15.991 0.505 | -0.201 3.360 0.041 | -0.036
ctr cos2
YouTube 43.965 0.337 |
WhatsApp 8.253 0.063 |
Twitter_X 0.714 0.005 |
Instagram 0.166 0.001 |
TikTok 1.138 0.009 |
Facebook 38.676 0.297 |
Linkedin 6.923 0.053 |
Telegram 0.165 0.001 |
Supplementary categories (the 10 first)
Dist Dim.1 cos2 v.test Dim.2 cos2
18_24 | 1.418 | 1.108 0.611 6.486 | -0.233 0.027
25_34 | 0.891 | 0.796 0.798 5.984 | -0.138 0.024
35_44 | 0.816 | 0.747 0.839 6.248 | 0.121 0.022
45_54 | 0.239 | 0.127 0.280 1.255 | 0.182 0.579
55_64 | 0.334 | -0.268 0.644 -2.525 | 0.138 0.172
65_+ | 0.992 | -0.930 0.879 -11.961 | -0.149 0.023
+Europa | 0.623 | 0.043 0.005 0.155 | -0.203 0.106
Alleanza Verdi-Sinistra | 0.445 | 0.392 0.776 1.836 | -0.092 0.042
Azione-Italia Viva | 0.419 | -0.075 0.032 -0.416 | -0.189 0.205
Did not vote | 0.240 | -0.090 0.141 -0.716 | -0.198 0.681
v.test Dim.3 cos2 v.test
18_24 -2.203 | 0.592 0.174 7.028 |
25_34 -1.681 | 0.077 0.007 1.175 |
35_44 1.634 | -0.085 0.011 -1.441 |
45_54 2.922 | -0.013 0.003 -0.256 |
55_64 2.111 | -0.076 0.052 -1.454 |
65_+ -3.098 | -0.092 0.009 -2.411 |
+Europa -1.169 | 0.110 0.031 0.799 |
Alleanza Verdi-Sinistra -0.695 | -0.033 0.006 -0.315 |
Azione-Italia Viva -1.705 | 0.115 0.076 1.302 |
Did not vote -2.544 | 0.070 0.086 1.132 |# 6.1.1 Variables relation to Dim1 and Dim2
PCA1_desc <- FactoMineR::dimdesc(PCA1_result, axes = 1:2, proba = 0.05)
PCA1_desc$Dim.1
Link between the variable and the continuous variables (R-square)
=================================================================================
correlation p.value
Telegram 0.7107833 1.041739e-200
Twitter_X 0.7038717 3.480574e-195
TikTok 0.6946969 4.276470e-188
Linkedin 0.6925912 1.661726e-186
Instagram 0.6881031 3.654681e-183
YouTube 0.6006333 2.210095e-128
Facebook 0.4590983 8.225746e-69
WhatsApp 0.3890403 2.895111e-48
Link between the variable and the categorical variable (1-way anova)
=============================================
R2 p.value
Age_cat 0.16327212 6.198418e-48
Inf_check 0.06810038 6.392291e-19
Inf_disagr 0.06628292 2.200328e-18
Inf_confirm 0.05654758 1.564022e-15
Left_Right 0.02422511 1.796380e-05
Vote 0.02712709 9.909615e-05
Link between variable and the categories of the categorical variables
================================================================
Estimate p.value
Age_cat=18_24 0.8448049 6.314004e-11
Age_cat=35_44 0.4837189 3.139509e-10
Age_cat=25_34 0.5330018 1.714502e-09
Inf_check=Inf_check_Very often 0.8497859 1.457769e-08
Inf_disagr=Inf_disagr_Very often 0.8356138 7.353664e-08
Inf_disagr=Inf_disagr_Often 0.4127128 1.234073e-05
Inf_confirm=Inf_confirm_Very often 0.6980323 1.294134e-05
Inf_check=Inf_check_Often 0.3993228 1.547763e-04
Vote=Forza Italia 0.8322058 3.113801e-04
Inf_confirm=Inf_confirm_Often 0.3868089 1.154004e-03
Left_Right=Ext_right 0.3924648 1.316871e-03
Vote=Movimento 5 Stelle 0.2151232 9.891616e-03
Left_Right=Right 0.2026920 2.072619e-02
Sex=Non-binary 1.0454188 4.902130e-02
Left_Right=No_position -0.3202688 3.374237e-02
Age_cat=55_64 -0.5312895 1.150529e-02
Left_Right=Left -0.3648603 3.203779e-03
Vote=Partito Democratico -0.4564384 2.558011e-04
Inf_confirm=Inf_confirm_Rarely -0.4923457 4.336972e-06
Inf_disagr=Inf_disagr_Rarely -0.4953230 3.847349e-06
Inf_check=Inf_check_Rarely -0.5263152 5.858813e-07
Inf_disagr=Inf_disagr_Never -0.7667999 2.008965e-08
Inf_check=Inf_check_Never -0.7816178 1.656179e-08
Inf_confirm=Inf_confirm_Never -0.7632705 2.767379e-09
Age_cat=65_+ -1.1933958 8.630145e-35PCA1_desc$Dim.2
Link between the variable and the continuous variables (R-square)
=================================================================================
correlation p.value
WhatsApp 0.7139505 2.701010e-203
Facebook 0.5233166 2.280502e-92
Instagram 0.3090819 3.383618e-30
TikTok -0.1627844 3.519636e-09
Telegram -0.2013846 2.266075e-13
Linkedin -0.3077723 6.067580e-30
Twitter_X -0.4057790 9.692650e-53
Link between the variable and the categorical variable (1-way anova)
=============================================
R2 p.value
Sex 0.037135620 2.156569e-11
Age_cat 0.020498489 6.094449e-05
Vote 0.022809086 9.041707e-04
Left_Right 0.011570927 1.961848e-02
Inf_check 0.008981723 1.971118e-02
Link between variable and the categories of the categorical variables
================================================================
Estimate p.value
Sex=Female 0.3038516 2.406963e-12
Age_cat=45_54 0.1952296 3.438103e-03
Left_Right=Ext_left 0.2183157 2.284974e-02
Vote=Partito Democratico 0.1603081 3.239774e-02
Age_cat=55_64 0.1515135 3.474535e-02
Age_cat=18_24 -0.2195176 2.757226e-02
Vote=Did not vote -0.1609720 1.091000e-02
Left_Right=No_position -0.1506509 1.028280e-02
Inf_check=Inf_check_Very often -0.2023231 1.938446e-03
Age_cat=65_+ -0.1358246 1.922577e-03
Vote=Other party -0.3415773 1.809898e-03
Sex=Male -0.1192441 4.004205e-12# 6.2 Initial biplot – active variables (social media platforms’ use)
p <- factoextra::fviz_pca_var(
PCA1_result,
col.var = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE
)Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
ℹ The deprecated feature was likely used in the ggpubr package.
Please report the issue at <https://github.com/kassambara/ggpubr/issues>.Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
ℹ Please use tidy evaluation idioms with `aes()`.
ℹ See also `vignette("ggplot2-in-packages")` for more information.
ℹ The deprecated feature was likely used in the factoextra package.
Please report the issue at <https://github.com/kassambara/factoextra/issues>.p
Centroids of Supplementary Qualitative Variables in the PCA of Social Media Platforms
# 7. Categorical Supplementary Variables (Centroids) --------------------
# Extract centroides of qualitative supplementary variables
get_sup_coords <- function(pca_result, variable_name, dims = 2) {
# 1) data extraction
quali_sup_data <- pca_result$call$quali.sup
if ("quali.sup" %in% names(quali_sup_data)) {
quali_sup_data <- quali_sup_data$quali.sup
}
# 2) check variables
if (!variable_name %in% names(quali_sup_data)) {
stop(paste("Variable", variable_name, "not found among supplementary qualitative variables."))
}
# 3) centroids (avg per category)
bary <- pca_result$call$quali.sup$barycentre
# Rows selection for variables categories
var_categories <- levels(quali_sup_data[[variable_name]])
bary_var <- bary[rownames(bary) %in% var_categories, , drop = FALSE]
if (nrow(bary_var) == 0) {
stop(paste("No barycentres found for variable", variable_name))
}
# 4) Avg and st.dev used in PCA
means <- pca_result$call$centre
sds <- pca_result$call$ecart.type
# Centroides standardization
bary_scaled <- sweep(bary_var, 2, means, "-")
bary_scaled <- sweep(bary_scaled, 2, sds, "/")
# 5) PCA axis projection
loadings <- pca_result$var$coord
projected <- as.matrix(bary_scaled) %*% as.matrix(loadings[, 1:dims])
# 6) Output in data frame
projected_df <- as.data.frame(projected)
colnames(projected_df) <- paste0("Dim.", 1:dims)
projected_df$Category <- rownames(projected_df)
projected_df$Variable <- variable_name
return(projected_df)
}
# Centroides coordinates per each supplementary variable
age_coords <- get_sup_coords(PCA1_result, "Age_cat")
vote_coords <- get_sup_coords(PCA1_result, "Vote")
Pol_int_coords <- get_sup_coords(PCA1_result, "Pol_int")
LRight_coords <- get_sup_coords(PCA1_result, "Left_Right")
Sex_coords <- get_sup_coords(PCA1_result, "Sex")
infdis_coords <- get_sup_coords(PCA1_result, "Inf_disagr")
infcheck_coords <- get_sup_coords(PCA1_result, "Inf_check")
infconf_coords <- get_sup_coords(PCA1_result, "Inf_confirm")
# 7.1 Biplot with vote centroids
p +
geom_point(
data = vote_coords,
aes(x = Dim.1, y = Dim.2),
color = "darkblue", size = 3, shape = 20
) +
ggrepel::geom_text_repel(
data = vote_coords,
aes(x = Dim.1, y = Dim.2, label = Category),
color = "darkblue",
size = 3.5
)
# 7.2 Biplot with age centroids
p +
geom_point(
data = age_coords,
aes(x = Dim.1, y = Dim.2),
color = "darkblue", size = 3, shape = 20
) +
ggrepel::geom_text_repel(
data = age_coords,
aes(x = Dim.1, y = Dim.2, label = Category),
color = "darkblue",
size = 3.5
)
# 7.4 Biplot with political interest centroids
p +
geom_point(
data = Pol_int_coords,
aes(x = Dim.1, y = Dim.2),
color = "darkblue", size = 3, shape = 20
) +
ggrepel::geom_text_repel(
data = Pol_int_coords,
aes(x = Dim.1, y = Dim.2, label = Category),
color = "darkblue",
size = 3.5
)
# 7.5 Biplot with ideological placement centroids
p +
geom_point(
data = LRight_coords,
aes(x = Dim.1, y = Dim.2),
color = "darkblue", size = 3, shape = 20
) +
ggrepel::geom_text_repel(
data = LRight_coords,
aes(x = Dim.1, y = Dim.2, label = Category),
color = "darkblue",
size = 3.5
)
# 7.6 Biplot with gender centroids
p +
geom_point(
data = Sex_coords,
aes(x = Dim.1, y = Dim.2),
color = "darkblue", size = 3, shape = 20
) +
ggrepel::geom_text_repel(
data = Sex_coords,
aes(x = Dim.1, y = Dim.2, label = Category),
color = "darkblue",
size = 3.5
)
# 7.7 Biplot with information disagreement centroids
p +
geom_point(
data = infdis_coords,
aes(x = Dim.1, y = Dim.2),
color = "darkblue", size = 3, shape = 20
) +
ggrepel::geom_text_repel(
data = infdis_coords,
aes(x = Dim.1, y = Dim.2, label = Category),
color = "darkblue",
size = 3.5
)
# 7.8 Biplot with inf_check centroids
p +
geom_point(
data = infcheck_coords,
aes(x = Dim.1, y = Dim.2),
color = "darkblue", size = 3, shape = 20
) +
ggrepel::geom_text_repel(
data = infcheck_coords,
aes(x = Dim.1, y = Dim.2, label = Category),
color = "darkblue",
size = 3.5
)
# 7.9 Biplot with info_confirmation centroids
p +
geom_point(
data = infconf_coords,
aes(x = Dim.1, y = Dim.2),
color = "darkblue", size = 3, shape = 20
) +
ggrepel::geom_text_repel(
data = infconf_coords,
aes(x = Dim.1, y = Dim.2, label = Category),
color = "darkblue",
size = 3.5
)
Principal Component Analysis Including Numerical Supplementary Variables: Political and Social Uses of Social Media
# 8. Numerical Supplementary Variables: Political/Social Uses of Social Media ----
# New dataset for PCA with following variables: inf_socially, inf_pol, sup_soc_causes, sup_pol_causes
data_PCA2 <- data_PCA1 %>%
dplyr::bind_cols(
data_any %>%
dplyr::select(inf_socially, inf_pol, sup_soc_causes, sup_pol_causes)
) %>%
# check numerical
dplyr::mutate(
dplyr::across(
c(inf_socially, inf_pol, sup_soc_causes, sup_pol_causes),
~ as.numeric(.x)
)
)
# Check
str(data_PCA2) tibble [1,301 × 20] (S3: tbl_df/tbl/data.frame)
$ Age_cat : Factor w/ 7 levels "0_17","18_24",..: 5 3 5 5 2 2 6 3 5 3 ...
..- attr(*, "label")= chr "EDAD RECODE"
$ Vote : Factor w/ 10 levels "+Europa","Alleanza Verdi-Sinistra",..: 7 NA 5 2 10 5 5 6 10 9 ...
$ Sex : Factor w/ 3 levels "Male","Female",..: 1 2 2 1 2 1 2 2 2 1 ...
..- attr(*, "label")= chr "Which gender do you identify with?"
$ Left_Right : Factor w/ 6 levels "Ext_left","Left",..: 3 NA 3 2 NA 3 6 NA 3 3 ...
$ Pol_int : Factor w/ 4 levels "Very interested",..: 3 3 4 2 3 2 3 2 3 2 ...
..- attr(*, "label")= chr "How interested would you say you are in politics - are you...?"
$ Inf_disagr : Factor w/ 5 levels "Very often","Often",..: 5 3 5 2 5 5 4 3 3 3 ...
..- attr(*, "label")= chr "Read, watch or listen to something online you disagree with_When looking for new"
$ Inf_check : Factor w/ 5 levels "Very often","Often",..: 5 3 5 2 5 5 4 3 3 2 ...
..- attr(*, "label")= chr "Check a news source that’s different from what you normally read, watch or lis"
$ Inf_confirm : Factor w/ 5 levels "Very often","Often",..: 5 3 5 2 5 5 4 3 3 2 ...
..- attr(*, "label")= chr "Try to confirm information you find by searching online for another source_When"
$ YouTube : int [1:1301] NA 2 7 3 7 6 6 5 4 6 ...
$ WhatsApp : int [1:1301] NA 6 7 6 6 5 5 6 6 5 ...
$ Twitter_X : int [1:1301] NA NA NA 1 1 1 1 6 NA 6 ...
$ Instagram : int [1:1301] NA 6 7 5 7 5 4 5 3 2 ...
$ TikTok : int [1:1301] NA NA 7 1 7 1 1 6 NA 6 ...
$ Facebook : int [1:1301] NA 6 7 5 1 1 5 6 5 5 ...
$ Linkedin : int [1:1301] NA 6 NA 4 1 1 1 4 NA 1 ...
$ Telegram : int [1:1301] NA 5 NA 1 2 1 1 5 NA 7 ...
$ inf_socially : num [1:1301] 0 1 0 1 0 0 0 1 1 1 ...
$ inf_pol : num [1:1301] 0 1 0 0 0 0 0 1 1 0 ...
$ sup_soc_causes: num [1:1301] 0 1 0 0 0 0 0 1 0 1 ...
$ sup_pol_causes: num [1:1301] 0 1 0 0 0 0 0 1 0 0 ...# NEW PCA estimation
#Active variables: the eight social media platforms
#Supplementary qualitative variables: Age_cat, Vote, Sex, Left_Right, Pol_int, Inf_disagr, Inf_check, and Inf_confirm.
#Supplementary quantitative variables: inf_socially, inf_pol, sup_soc_causes, and sup_pol_causes.
PCA2_result <- FactoMineR::PCA(
data_PCA2,
quanti.sup = 17:20, # supplementari numeriche
quali.sup = 1:8, # supplementari qualitative
graph = FALSE
)Warning in FactoMineR::PCA(data_PCA2, quanti.sup = 17:20, quali.sup = 1:8, :
Missing values are imputed by the mean of the variable: you should use the
imputePCA function of the missMDA package# Biplot of active variables with numerical supplementary variables
factoextra::fviz_pca_var(
PCA2_result,
col.var = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE
)
Factor Analysis of online and offline political participation
# 9. Factor Analysis of online and offline political participation ----
# 9.0 Dictionary: original items -> new variable names -> activities ----
participation_dict <- tibble::tibble(
var_bin = c(
# --- ONLINE (13) ---
"parton_1", "parton_2", "parton_3", "parton_4", "parton_5",
"parton_6", "parton_7", "parton_8", "parton_9", "parton_10",
"parton_11", "parton_12", "parton_13",
# --- OFFLINE (13) ---
"partoff_1", "partoff_2", "partoff_3", "partoff_4", "partoff_5",
"partoff_6", "partoff_7", "partoff_8", "partoff_9", "partoff_10",
"partoff_11", "partoff_12", "partoff_13"
),
new_name = c(
# --- ONLINE ---
"pol_inf_on", # parton_1 – actively searched for political information online
"liked_on", # parton_2 – liked/followed political content
"shared_on", # parton_3 – shared/retweeted political content
"posted_on", # parton_4 – posted own political content
"discuss_on", # parton_5 – discussed politics online
"comment_on", # parton_6 – commented on political content
"profile_on", # parton_7 – changed profile picture/status for political reasons
"vol_party_on", # parton_8 – volunteered online for party/candidate
"donat_party_on", # parton_9 – donated money online to party/candidate
"donat_NGO_on", # parton_10 – donated money online to NGO/association
"vol_social_on", # parton_11 – volunteered online for NGO/association
"contact_on", # parton_12 – contacted politician/official online
"petition_on", # parton_13 – signed online petition
# --- OFFLINE ---
"pol_meet_off", # partoff_1 – attended political meeting/rally
"strike_off", # partoff_2 – took part in strike
"demonst_off", # partoff_3 – took part in demonstration/march
"org_demonst_off", # partoff_4 – helped organise demonstration
"discuss_off", # partoff_5 – discussed politics face-to-face
"vol_party_off", # partoff_6 – volunteered offline for party/candidate
"donat_party_off", # partoff_7 – donated money offline to party/candidate
"donat_NGO_off", # partoff_8 – donated money offline to NGO/association
"contact_off", # partoff_9 – contacted politician/official offline
"read_campaign_off", # partoff_10 – read leaflets/campaign material
"petition_off", # partoff_11 – signed paper petition
"boycott_off", # partoff_12 – (boy)cott/buycott participation
"button_off" # partoff_13 – wore badge/button/sticker
),
activity = c(
# --- ONLINE (descrizione sintetica) ---
"Search political information online",
"Like/follow political content",
"Share/retweet political content",
"Post own political content",
"Discuss politics online",
"Comment on political content",
"Change profile/status for politics",
"Volunteer online for party/candidate",
"Donate money online to party/candidate",
"Donate money online to NGO/association",
"Volunteer online for NGO/association",
"Contact politician/official online",
"Sign online petition",
# --- OFFLINE ---
"Attend political meeting/rally",
"Take part in strike",
"Take part in demonstration/march",
"Help organise demonstration",
"Discuss politics face-to-face",
"Volunteer offline for party/candidate",
"Donate money offline to party/candidate",
"Donate money offline to NGO/association",
"Contact politician/official offline",
"Read leaflets/campaign material",
"Sign paper petition",
"Take part in (boy)cott/buycott",
"Wear badge/button/sticker"
)
)
# 9.1 Build data_FACTOR with online + offline items ---------------------
data_FACTOR <- EUPeopleNet %>%
dplyr::mutate(id = dplyr::row_number()) %>%
dplyr::select(
id,
dplyr::all_of(participation_dict$var_bin)
)
# Values are already 0/1; in case "9 = prefer not to answer" is present, recode to NA
data_FACTOR <- data_FACTOR %>%
dplyr::mutate(
dplyr::across(
-id,
~ dplyr::na_if(.x, 9)
)
) %>%
dplyr::mutate(
dplyr::across(
-id,
~ as.numeric(.x)
)
)
# >>> QUI LA CORREZIONE IMPORTANTE <<<
# new_name = old_name
rename_vec <- stats::setNames(
participation_dict$var_bin, # valori = vecchi nomi (parton_1, partoff_1, ...)
participation_dict$new_name # nomi = nuovi nomi (pol_inf_on, pol_meet_off, ...)
)
data_FACTOR <- data_FACTOR %>%
dplyr::rename(!!!rename_vec)
# 9.2 Prepare and test suitability for Factor Analysis ------------------
# Drop rows with too many missing values (> 50%)
data_FACTOR <- data_FACTOR %>%
dplyr::filter(
rowMeans(is.na(dplyr::across(-id))) < 0.5
)
# List of item variables
part_vars <- setdiff(names(data_FACTOR), "id")
# Correlation matrix (pairwise)
cor_matrix_part <- cor(
dplyr::select(data_FACTOR, dplyr::all_of(part_vars)),
use = "pairwise.complete.obs"
)
# KMO
psych::KMO(cor_matrix_part)Kaiser-Meyer-Olkin factor adequacy
Call: psych::KMO(r = cor_matrix_part)
Overall MSA = 0.92
MSA for each item =
pol_inf_on liked_on shared_on posted_on
0.89 0.91 0.94 0.95
discuss_on comment_on profile_on vol_party_on
0.91 0.91 0.95 0.95
donat_party_on donat_NGO_on vol_social_on contact_on
0.94 0.82 0.95 0.93
petition_on pol_meet_off strike_off demonst_off
0.92 0.95 0.95 0.94
org_demonst_off discuss_off vol_party_off donat_party_off
0.94 0.86 0.93 0.94
donat_NGO_off contact_off read_campaign_off petition_off
0.82 0.91 0.93 0.92
boycott_off button_off
0.95 0.94 # Bartlett’s test
psych::cortest.bartlett(
cor_matrix_part,
n = nrow(data_FACTOR)
)$chisq
[1] 10451.93
$p.value
[1] 0
$df
[1] 325# 9.3 Exploratory Factor Analysis --------------------------------------
# Polychoric correlation (good for binary/ordinal items)
cor_poly <- psych::polychoric(
dplyr::select(data_FACTOR, dplyr::all_of(part_vars))
)$rho
# Parallel analysis (decide nfactors; here we keep 5 as in Spanish script)
fa.parallel(
cor_poly,
n.obs = nrow(data_FACTOR),
fm = "minres",
fa = "fa"
)
Parallel analysis suggests that the number of factors = 8 and the number of components = NA # EFA with 5 factors, oblimin rotation
efa_result <- psych::fa(
dplyr::select(data_FACTOR, dplyr::all_of(part_vars)),
nfactors = 5,
rotate = "oblimin",
fm = "minres",
scores = "regression"
)
# Inspect loadings (only > .30)
print(efa_result$loadings, cutoff = 0.30)
Loadings:
MR1 MR2 MR3 MR5 MR4
pol_inf_on 0.309 0.476
liked_on 0.509
shared_on 0.555
posted_on 0.540
discuss_on 0.708
comment_on 0.682
profile_on 0.440
vol_party_on 0.484
donat_party_on 0.333
donat_NGO_on 0.792
vol_social_on
contact_on 0.535
petition_on
pol_meet_off 0.314 0.300
strike_off 0.455
demonst_off 0.617
org_demonst_off 0.488
discuss_off 0.483
vol_party_off 0.687
donat_party_off 0.432
donat_NGO_off 0.715
contact_off 0.759
read_campaign_off 0.408
petition_off 0.393
boycott_off 0.302
button_off 0.610
MR1 MR2 MR3 MR5 MR4
SS loadings 2.932 2.130 1.417 1.176 1.148
Proportion Var 0.113 0.082 0.055 0.045 0.044
Cumulative Var 0.113 0.195 0.249 0.294 0.339# Variance explained and fit indices
efa_result$Vaccounted MR1 MR2 MR3 MR5 MR4
SS loadings 3.5954713 2.58411074 1.68179565 1.60764309 1.32603612
Proportion Var 0.1382874 0.09938887 0.06468445 0.06183243 0.05100139
Cumulative Var 0.1382874 0.23767623 0.30236068 0.36419311 0.41519450
Proportion Explained 0.3330665 0.23937908 0.15579313 0.14892400 0.12283734
Cumulative Proportion 0.3330665 0.57244553 0.72823865 0.87716266 1.00000000efa_result$TLI[1] 0.917059efa_result$RMSEA RMSEA lower upper confidence
0.04523795 0.04174861 0.04882587 0.90000000 # Optional: diagram
psych::fa.diagram(
efa_result,
main = "Exploratory Factor Analysis – Political Participation",
simple = FALSE
)
# Optional: 2D plot of first two factors
loadings_df <- as.data.frame(efa_result$loadings[1:length(part_vars), ])
loadings_df$variable <- rownames(loadings_df)
ggplot(loadings_df, aes(x = MR1, y = MR2, label = variable)) +
geom_point(color = "steelblue", size = 3) +
geom_text(vjust = -0.5, size = 3) +
geom_hline(yintercept = 0, linetype = "dashed", color = "gray60") +
geom_vline(xintercept = 0, linetype = "dashed", color = "gray60") +
theme_minimal() +
labs(
title = "Factor Loadings (EFA)",
x = "Factor 1",
y = "Factor 2"
)
# 9.4 Save factor scores and merge with PCA dataset --------------------
# Extract factor scores
factor_scores <- as.data.frame(efa_result$scores)
# Rename factors
factor_scores <- factor_scores %>%
dplyr::rename(
Info_Online = MR1,
Partisan_mobil = MR2,
Civic_Engagement = MR3,
Protest_Action = MR4,
Info_Offline = MR5
) %>%
dplyr::mutate(id = data_FACTOR$id)
# Merge factor scores into PCA dataset (data_PCA2 -> data_PCA3)
data_PCA3 <- data_PCA2 %>%
dplyr::mutate(id = dplyr::row_number()) %>%
dplyr::left_join(factor_scores, by = "id") %>%
dplyr::select(-id)New PCA with factors as supplementary numerical variables
# 10. New PCA with factors as supplementary numerical variables ---------
# Optional check to see if variables exist
names(data_PCA3) [1] "Age_cat" "Vote" "Sex" "Left_Right"
[5] "Pol_int" "Inf_disagr" "Inf_check" "Inf_confirm"
[9] "YouTube" "WhatsApp" "Twitter_X" "Instagram"
[13] "TikTok" "Facebook" "Linkedin" "Telegram"
[17] "inf_socially" "inf_pol" "sup_soc_causes" "sup_pol_causes"
[21] "Info_Online" "Partisan_mobil" "Civic_Engagement" "Info_Offline"
[25] "Protest_Action" # vectors of indexes FactoMineR::PCA
quali_sup_idx <- c(
which(names(data_PCA3) == "Age_cat"),
which(names(data_PCA3) == "Vote"),
which(names(data_PCA3) == "Sex"),
which(names(data_PCA3) == "Left_Right"),
which(names(data_PCA3) == "Pol_int"),
which(names(data_PCA3) == "Inf_disagr"),
which(names(data_PCA3) == "Inf_check"),
which(names(data_PCA3) == "Inf_confirm")
)
quanti_sup_idx <- c(
which(names(data_PCA3) == "inf_socially"),
which(names(data_PCA3) == "inf_pol"),
which(names(data_PCA3) == "sup_soc_causes"),
which(names(data_PCA3) == "sup_pol_causes"),
which(names(data_PCA3) == "Info_Online"),
which(names(data_PCA3) == "Partisan_mobil"),
which(names(data_PCA3) == "Civic_Engagement"),
which(names(data_PCA3) == "Protest_Action"),
which(names(data_PCA3) == "Info_Offline")
)
# PCA3: active platforms, specific uses + supplementary factors
PCA3_result <- FactoMineR::PCA(
data_PCA3,
quali.sup = quali_sup_idx,
quanti.sup = quanti_sup_idx,
graph = FALSE
)Warning in FactoMineR::PCA(data_PCA3, quali.sup = quali_sup_idx, quanti.sup =
quanti_sup_idx, : Missing values are imputed by the mean of the variable: you
should use the imputePCA function of the missMDA package# Summary of the PCA:
summary(PCA3_result)
Call:
FactoMineR::PCA(X = data_PCA3, quanti.sup = quanti_sup_idx, quali.sup = quali_sup_idx,
graph = FALSE)
Eigenvalues
Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
Variance 3.159 1.207 0.768 0.698 0.620 0.572 0.518
% of var. 39.491 15.090 9.595 8.722 7.749 7.150 6.474
Cumulative % of var. 39.491 54.581 64.176 72.898 80.647 87.797 94.271
Dim.8
Variance 0.458
% of var. 5.729
Cumulative % of var. 100.000
Individuals (the 10 first)
Dist Dim.1 ctr cos2 Dim.2 ctr cos2
1 | 0.000 | 0.000 0.000 0.222 | 0.000 0.000 0.956
2 | 2.944 | 1.380 0.046 0.220 | -0.175 0.002 0.004
3 | 3.457 | 2.479 0.150 0.514 | 1.286 0.105 0.138
4 | 1.917 | -0.564 0.008 0.087 | 0.551 0.019 0.083
5 | 3.821 | 0.725 0.013 0.036 | -0.106 0.001 0.001
6 | 2.831 | -1.359 0.045 0.230 | -0.369 0.009 0.017
7 | 2.084 | -1.008 0.025 0.234 | 0.477 0.014 0.052
8 | 3.095 | 2.772 0.187 0.802 | -0.867 0.048 0.078
9 | 0.537 | -0.138 0.000 0.066 | 0.033 0.000 0.004
10 | 3.723 | 1.833 0.082 0.242 | -1.668 0.177 0.201
Dim.3 ctr cos2
1 | 0.000 0.000 0.002 |
2 | -1.875 0.352 0.406 |
3 | 0.837 0.070 0.059 |
4 | -0.767 0.059 0.160 |
5 | 2.811 0.791 0.541 |
6 | 1.932 0.374 0.466 |
7 | 0.679 0.046 0.106 |
8 | -0.337 0.011 0.012 |
9 | -0.098 0.001 0.033 |
10 | 0.635 0.040 0.029 |
Variables
Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3
YouTube | 0.601 11.419 0.361 | 0.040 0.136 0.002 | 0.581
WhatsApp | 0.389 4.791 0.151 | 0.714 42.224 0.510 | 0.252
Twitter_X | 0.704 15.682 0.495 | -0.406 13.640 0.165 | -0.074
Instagram | 0.688 14.987 0.473 | 0.309 7.914 0.096 | -0.036
TikTok | 0.695 15.276 0.483 | -0.163 2.195 0.026 | 0.093
Facebook | 0.459 6.671 0.211 | 0.523 22.686 0.274 | -0.545
Linkedin | 0.693 15.183 0.480 | -0.308 7.847 0.095 | -0.231
Telegram | 0.711 15.991 0.505 | -0.201 3.360 0.041 | -0.036
ctr cos2
YouTube 43.965 0.337 |
WhatsApp 8.253 0.063 |
Twitter_X 0.714 0.005 |
Instagram 0.166 0.001 |
TikTok 1.138 0.009 |
Facebook 38.676 0.297 |
Linkedin 6.923 0.053 |
Telegram 0.165 0.001 |
Supplementary continuous variables
Dim.1 cos2 Dim.2 cos2 Dim.3 cos2
inf_socially | 0.330 0.109 | 0.112 0.013 | 0.046 0.002 |
inf_pol | 0.348 0.121 | 0.037 0.001 | 0.096 0.009 |
sup_soc_causes | 0.356 0.127 | 0.066 0.004 | 0.052 0.003 |
sup_pol_causes | 0.359 0.129 | 0.049 0.002 | 0.055 0.003 |
Info_Online | 0.264 0.070 | -0.078 0.006 | -0.052 0.003 |
Partisan_mobil | 0.270 0.073 | -0.005 0.000 | -0.017 0.000 |
Civic_Engagement | 0.095 0.009 | -0.084 0.007 | -0.009 0.000 |
Protest_Action | 0.017 0.000 | 0.034 0.001 | 0.059 0.004 |
Info_Offline | 0.230 0.053 | -0.058 0.003 | -0.073 0.005 |
Supplementary categories (the 10 first)
Dist Dim.1 cos2 v.test Dim.2 cos2
18_24 | 1.418 | 1.108 0.611 6.486 | -0.233 0.027
25_34 | 0.891 | 0.796 0.798 5.984 | -0.138 0.024
35_44 | 0.816 | 0.747 0.839 6.248 | 0.121 0.022
45_54 | 0.239 | 0.127 0.280 1.255 | 0.182 0.579
55_64 | 0.334 | -0.268 0.644 -2.525 | 0.138 0.172
65_+ | 0.992 | -0.930 0.879 -11.961 | -0.149 0.023
+Europa | 0.623 | 0.043 0.005 0.155 | -0.203 0.106
Alleanza Verdi-Sinistra | 0.445 | 0.392 0.776 1.836 | -0.092 0.042
Azione-Italia Viva | 0.419 | -0.075 0.032 -0.416 | -0.189 0.205
Did not vote | 0.240 | -0.090 0.141 -0.716 | -0.198 0.681
v.test Dim.3 cos2 v.test
18_24 -2.203 | 0.592 0.174 7.028 |
25_34 -1.681 | 0.077 0.007 1.175 |
35_44 1.634 | -0.085 0.011 -1.441 |
45_54 2.922 | -0.013 0.003 -0.256 |
55_64 2.111 | -0.076 0.052 -1.454 |
65_+ -3.098 | -0.092 0.009 -2.411 |
+Europa -1.169 | 0.110 0.031 0.799 |
Alleanza Verdi-Sinistra -0.695 | -0.033 0.006 -0.315 |
Azione-Italia Viva -1.705 | 0.115 0.076 1.302 |
Did not vote -2.544 | 0.070 0.086 1.132 |# Description of the dimensions of the PCA:
PCA3_desc <- FactoMineR::dimdesc(PCA3_result, axes = 1:2, proba = 0.05)
# First dimension
PCA3_desc$Dim.1
Link between the variable and the continuous variables (R-square)
=================================================================================
correlation p.value
Telegram 0.71078325 1.041739e-200
Twitter_X 0.70387174 3.480574e-195
TikTok 0.69469692 4.276470e-188
Linkedin 0.69259115 1.661726e-186
Instagram 0.68810309 3.654681e-183
YouTube 0.60063333 2.210095e-128
Facebook 0.45909834 8.225746e-69
WhatsApp 0.38904027 2.895111e-48
sup_pol_causes 0.35876171 8.389189e-41
sup_soc_causes 0.35589341 3.890533e-40
inf_pol 0.34752598 3.125067e-38
inf_socially 0.32963437 2.387166e-34
Partisan_mobil 0.27029646 3.228874e-23
Info_Online 0.26409260 3.357295e-22
Info_Offline 0.22951271 5.178604e-17
Civic_Engagement 0.09532225 5.758285e-04
Link between the variable and the categorical variable (1-way anova)
=============================================
R2 p.value
Age_cat 0.16327212 6.198418e-48
Inf_check 0.06810038 6.392291e-19
Inf_disagr 0.06628292 2.200328e-18
Inf_confirm 0.05654758 1.564022e-15
Left_Right 0.02422511 1.796380e-05
Vote 0.02712709 9.909615e-05
Link between variable and the categories of the categorical variables
================================================================
Estimate p.value
Age_cat=18_24 0.8448049 6.314004e-11
Age_cat=35_44 0.4837189 3.139509e-10
Age_cat=25_34 0.5330018 1.714502e-09
Inf_check=Inf_check_Very often 0.8497859 1.457769e-08
Inf_disagr=Inf_disagr_Very often 0.8356138 7.353664e-08
Inf_disagr=Inf_disagr_Often 0.4127128 1.234073e-05
Inf_confirm=Inf_confirm_Very often 0.6980323 1.294134e-05
Inf_check=Inf_check_Often 0.3993228 1.547763e-04
Vote=Forza Italia 0.8322058 3.113801e-04
Inf_confirm=Inf_confirm_Often 0.3868089 1.154004e-03
Left_Right=Ext_right 0.3924648 1.316871e-03
Vote=Movimento 5 Stelle 0.2151232 9.891616e-03
Left_Right=Right 0.2026920 2.072619e-02
Sex=Non-binary 1.0454188 4.902130e-02
Left_Right=No_position -0.3202688 3.374237e-02
Age_cat=55_64 -0.5312895 1.150529e-02
Left_Right=Left -0.3648603 3.203779e-03
Vote=Partito Democratico -0.4564384 2.558011e-04
Inf_confirm=Inf_confirm_Rarely -0.4923457 4.336972e-06
Inf_disagr=Inf_disagr_Rarely -0.4953230 3.847349e-06
Inf_check=Inf_check_Rarely -0.5263152 5.858813e-07
Inf_disagr=Inf_disagr_Never -0.7667999 2.008965e-08
Inf_check=Inf_check_Never -0.7816178 1.656179e-08
Inf_confirm=Inf_confirm_Never -0.7632705 2.767379e-09
Age_cat=65_+ -1.1933958 8.630145e-35# Second dimension
PCA3_desc$Dim.2
Link between the variable and the continuous variables (R-square)
=================================================================================
correlation p.value
WhatsApp 0.71395047 2.701010e-203
Facebook 0.52331660 2.280502e-92
Instagram 0.30908189 3.383618e-30
inf_socially 0.11211703 5.058913e-05
sup_soc_causes 0.06568003 1.782087e-02
Info_Offline -0.05801716 3.640328e-02
Info_Online -0.07777316 5.003858e-03
Civic_Engagement -0.08401083 2.424021e-03
TikTok -0.16278444 3.519636e-09
Telegram -0.20138465 2.266075e-13
Linkedin -0.30777231 6.067580e-30
Twitter_X -0.40577897 9.692650e-53
Link between the variable and the categorical variable (1-way anova)
=============================================
R2 p.value
Sex 0.037135620 2.156569e-11
Age_cat 0.020498489 6.094449e-05
Vote 0.022809086 9.041707e-04
Left_Right 0.011570927 1.961848e-02
Inf_check 0.008981723 1.971118e-02
Link between variable and the categories of the categorical variables
================================================================
Estimate p.value
Sex=Female 0.3038516 2.406963e-12
Age_cat=45_54 0.1952296 3.438103e-03
Left_Right=Ext_left 0.2183157 2.284974e-02
Vote=Partito Democratico 0.1603081 3.239774e-02
Age_cat=55_64 0.1515135 3.474535e-02
Age_cat=18_24 -0.2195176 2.757226e-02
Vote=Did not vote -0.1609720 1.091000e-02
Left_Right=No_position -0.1506509 1.028280e-02
Inf_check=Inf_check_Very often -0.2023231 1.938446e-03
Age_cat=65_+ -0.1358246 1.922577e-03
Vote=Other party -0.3415773 1.809898e-03
Sex=Male -0.1192441 4.004205e-12# New biplot with factors as supplementary numerical variables
factoextra::fviz_pca_var(
PCA3_result,
col.var = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE
)Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
Canonical Correlation Analysis
# 11. Canonical Correlation Analysis (CCA) -------------------
# relationship between platform uses (3 PCA COMPONENTS) & latent dimensions of political participation (5 EFA factors)
social_vars <- c("YouTube","WhatsApp","Twitter_X","Instagram",
"TikTok","Facebook","Linkedin","Telegram")
factor_vars <- c("Info_Online", "Partisan_mobil",
"Civic_Engagement", "Protest_Action", "Info_Offline")
# check only complete cases
complete_ids <- complete.cases(
data_PCA3[, social_vars],
data_PCA3[, factor_vars]
)
message("✅ Using ", sum(complete_ids),
" respondents with complete data on platforms and factors.")✅ Using 995 respondents with complete data on platforms and factors.pca_data <- data_PCA3[complete_ids, social_vars]
efa_data <- data_PCA3[complete_ids, factor_vars]
# 11.1 PCA platforms (3 components, as per ES script analyses)
pca_res <- psych::principal(
pca_data,
nfactors = 3,
rotate = "none",
scores = TRUE
)
pca_scores <- as.data.frame(pca_res$scores)
colnames(pca_scores) <- paste0("PC", 1:3) # PC1, PC2, PC3
# Participation factors (already estimated) – standardized
efa_scores <- as.data.frame(scale(efa_data))
# 11.2 Correlation matrix of PCA components e EFA factors
cor_matrix_pca_efa <- cor(
cbind(pca_scores, efa_scores),
use = "pairwise.complete.obs"
)
corrplot::corrplot(
cor_matrix_pca_efa,
method = "color",
type = "upper",
tl.col = "black",
tl.cex = 0.8,
addCoef.col = "black",
number.cex = 0.6,
title = "Correlations between PCA Components and EFA Factors",
mar = c(0,0,2,0)
)
# 11.3 Canonical Correlation Analysis (CCA)
if (!requireNamespace("CCA", quietly = TRUE)) {
stop("Il pacchetto 'CCA' non è installato. Esegui: install.packages('CCA')")
}
if (!requireNamespace("CCP", quietly = TRUE)) {
stop("Il pacchetto 'CCP' non è installato. Esegui: install.packages('CCP')")
}
# Blocks for CCA
X_pca <- pca_scores # 3 PCA components
Y_efa <- efa_scores # 5 EFA factors
dim(X_pca)[1] 995 3dim(Y_efa)[1] 995 5# 11.3.1 CCA execution
cca_result <- CCA::cc(X_pca, Y_efa)
# CCA
cca_result$cor[1] 0.39421385 0.13937680 0.05488145# loadings
cca_result$xcoef # for PCA components [,1] [,2] [,3]
PC1 -0.9428084 0.3178548 -0.1004023
PC2 0.2275569 0.8338335 0.5029309
PC3 -0.2435779 -0.4513202 0.8584753cca_result$ycoef # for EFA factors [,1] [,2] [,3]
Info_Online -0.48913811 -0.2289978 -0.87115583
Partisan_mobil -0.65513891 0.8300949 -0.24011844
Civic_Engagement 0.07386196 -0.7479003 -0.45467961
Protest_Action 0.33416540 0.2894763 0.03472063
Info_Offline -0.12160833 -0.2787729 1.47357619# 11.3.2 Significance test (Wilks’ Lambda)
rho <- cca_result$cor
n <- nrow(X_pca)
p <- ncol(X_pca)
q <- ncol(Y_efa)
cca_test <- CCP::p.asym(rho, n, p, q, tstat = "Wilks")Wilks' Lambda, using F-approximation (Rao's F):
stat approx df1 df2 p.value
1 to 3: 0.8256939 13.0521557 15 2725.074 0.000000000
2 to 3: 0.9776206 2.8111228 8 1976.000 0.004232957
3 to 3: 0.9969880 0.9959472 3 989.000 0.393967752cca_test$id
[1] "Wilks"
$stat
[1] 0.8256939 0.9776206 0.9969880
$approx
[1] 13.0521557 2.8111228 0.9959472
$df1
[1] 15 8 3
$df2
[1] 2725.074 1976.000 989.000
$p.value
[1] 0.000000000 0.004232957 0.393967752# 11.4 Visualization
# Plot of Canonical Variates
U <- as.data.frame(as.matrix(scale(X_pca)) %*% cca_result$xcoef)
V <- as.data.frame(as.matrix(scale(Y_efa)) %*% cca_result$ycoef)
colnames(U) <- paste0("U", 1:ncol(U))
colnames(V) <- paste0("V", 1:ncol(V))
cca_df <- cbind(U, V)
# 11.4.1 Canonical Correlation between PCA (Social Media) and EFA (Political Engagement)
ggplot(cca_df, aes(x = U1, y = V1)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", color = "coral") +
labs(
title = "Canonical Correlation between PCA (Social Media) and EFA (Political Engagement)",
x = "First Canonical Variate (PCA block)",
y = "First Canonical Variate (EFA block)"
) +
theme_minimal()`geom_smooth()` using formula = 'y ~ x'
# 11.4.2 Canonical Correlation (all functions)
data.frame(
Canonical_Function = 1:length(cca_result$cor),
Correlation = cca_result$cor
) %>%
ggplot(aes(x = Canonical_Function, y = Correlation)) +
geom_col(fill = "coral") +
geom_text(aes(label = round(Correlation, 2)), vjust = -0.3) +
labs(
title = "Canonical Correlations between PCA and EFA Latent Sets",
x = "Canonical Function",
y = "Correlation"
) +
theme_minimal()
Hierarchical clustering on PCA results
# 12. Hierarchical clustering on PCA results
# 12.1 HCPC su PCA2_result -------------
hcpc_res <- FactoMineR::HCPC(
PCA2_result,
nb.clust = -1,
graph = FALSE
)
# Dataset structure
str(hcpc_res$call$X)'data.frame': 1301 obs. of 6 variables:
$ Dim.1: num -3.76 -3.76 -3.76 -3.76 -3.76 ...
$ Dim.2: num -3.04 -3.04 -3.04 -3.04 -3.04 ...
$ Dim.3: num -0.869 -0.869 -0.869 -0.869 -0.869 ...
$ Dim.4: num 0.173 0.173 0.173 0.173 0.173 ...
$ Dim.5: num -1.01 -1.01 -1.01 -1.01 -1.01 ...
$ clust: Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...# Cluster description (quantitative and qualitative variables)
print(hcpc_res$desc.var$quanti)$`1`
v.test Mean in category Overall mean sd in category
inf_pol -8.619589 0.4444444 0.6610300 0.4969040
sup_pol_causes -8.638983 0.3046595 0.5334358 0.4602631
Twitter_X -8.647238 1.5208886 2.3879870 1.2570367
sup_soc_causes -9.494062 0.3118280 0.5618755 0.4632400
Linkedin -9.557636 1.4937609 2.3449111 1.0114933
inf_socially -10.029012 0.5017921 0.7363566 0.4999968
TikTok -10.676741 1.3567245 2.5052293 1.0014777
Telegram -11.251364 1.7333518 2.9435939 1.3722745
YouTube -11.756496 3.0788530 4.1284047 1.6872548
WhatsApp -18.754809 4.5585063 5.8232558 1.9894849
Instagram -20.772240 1.6525179 4.0525078 1.3191793
Facebook -23.084470 2.2688172 4.6645963 1.7748125
Overall sd p.value
inf_pol 0.4733596 6.719496e-18
sup_pol_causes 0.4988808 5.671564e-18
Twitter_X 1.8890319 5.276066e-18
sup_soc_causes 0.4961567 2.222026e-21
Linkedin 1.6776605 1.204775e-21
inf_socially 0.4406081 1.136478e-23
TikTok 2.0264803 1.307819e-26
Telegram 2.0263543 2.280375e-29
YouTube 1.6817996 6.539233e-32
WhatsApp 1.2703992 1.768531e-78
Instagram 2.1765789 7.717460e-96
Facebook 1.9551280 6.631616e-118
$`2`
v.test Mean in category Overall mean sd in category Overall sd
WhatsApp 11.460297 6.2032356 5.8232558 0.5104873 1.2703992
Facebook 11.325052 5.2424791 4.6645963 1.4653822 1.9551280
Instagram 4.132437 4.2872575 4.0525078 2.0144862 2.1765789
inf_socially 2.131661 0.7608696 0.7363566 0.4265525 0.4406081
YouTube -3.064133 3.9939097 4.1284047 1.5427273 1.6817996
Telegram -9.914354 2.4192645 2.9435939 1.7175343 2.0263543
TikTok -10.546046 1.9474576 2.5052293 1.5944161 2.0264803
Linkedin -12.174441 1.8118494 2.3449111 1.1343184 1.6776605
Twitter_X -14.666328 1.6649092 2.3879870 1.1924886 1.8890319
p.value
WhatsApp 2.087968e-30
Facebook 9.862073e-30
Instagram 3.589379e-05
inf_socially 3.303475e-02
YouTube 2.183022e-03
Telegram 3.605820e-23
TikTok 5.298021e-26
Linkedin 4.252967e-34
Twitter_X 1.059250e-48
$`3`
v.test Mean in category Overall mean sd in category
Twitter_X 24.930347 4.6194453 2.3879870 1.7327380
Linkedin 22.934829 4.1680543 2.3449111 1.7239945
TikTok 22.124288 4.6296126 2.5052293 1.9230664
Telegram 21.942131 5.0503553 2.9435939 1.4616608
Instagram 14.825351 5.5814835 4.0525078 1.1727849
YouTube 14.575831 5.2899301 4.1284047 1.1968981
sup_soc_causes 9.155220 0.7771084 0.5618755 0.4161862
sup_pol_causes 9.034122 0.7469880 0.5334358 0.4347378
Facebook 8.768383 5.4768952 4.6645963 1.3007396
inf_pol 8.666931 0.8554217 0.6610300 0.3516752
inf_socially 7.001561 0.8825301 0.7363566 0.3219794
WhatsApp 4.537405 6.0963855 5.8232558 0.8588933
Overall sd p.value
Twitter_X 1.8890319 3.488870e-137
Linkedin 1.6776605 2.088283e-116
TikTok 2.0264803 1.845264e-108
Telegram 2.0263543 1.029628e-106
Instagram 2.1765789 1.004540e-49
YouTube 1.6817996 4.002215e-48
sup_soc_causes 0.4961567 5.424727e-20
sup_pol_causes 0.4988808 1.653240e-19
Facebook 1.9551280 1.812509e-18
inf_pol 0.4733596 4.439233e-18
inf_socially 0.4406081 2.531263e-12
WhatsApp 1.2703992 5.695056e-06print(hcpc_res$desc.var$quali)NULL# Cluster map on PCA
plot(hcpc_res, choice = "map")
# Dendrogram
plot(hcpc_res, choice = "tree")
# 12.2 Cluster assignment--------
clusters <- hcpc_res$data.clust %>%
dplyr::select(clust) %>%
dplyr::mutate(ID = dplyr::row_number())
data_clustered <- data_PCA3 %>%
dplyr::mutate(ID = dplyr::row_number()) %>%
dplyr::left_join(clusters, by = "ID")
# 12.3 Variables identification ----------------
num_vars <- names(dplyr::select(data_clustered, where(is.numeric))) %>%
setdiff("ID")
cat_vars <- names(dplyr::select(data_clustered, where(is.factor)))
# 12.4 Summarize of variables by cluster -------------
cat("===== Quantitative Variables by Cluster =====\n")===== Quantitative Variables by Cluster =====num_summary <- data_clustered %>%
group_by(clust) %>%
summarise(
across(all_of(num_vars),
list(mean = mean, sd = sd),
.names = "{.col}_{.fn}"),
.groups = "drop"
)
print(num_summary)# A tibble: 3 × 35
clust YouTube_mean YouTube_sd WhatsApp_mean WhatsApp_sd Twitter_X_mean
<fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 3.08 1.69 NA NA NA
2 2 NA NA NA NA NA
3 3 NA NA 6.10 0.860 NA
# ℹ 29 more variables: Twitter_X_sd <dbl>, Instagram_mean <dbl>,
# Instagram_sd <dbl>, TikTok_mean <dbl>, TikTok_sd <dbl>,
# Facebook_mean <dbl>, Facebook_sd <dbl>, Linkedin_mean <dbl>,
# Linkedin_sd <dbl>, Telegram_mean <dbl>, Telegram_sd <dbl>,
# inf_socially_mean <dbl>, inf_socially_sd <dbl>, inf_pol_mean <dbl>,
# inf_pol_sd <dbl>, sup_soc_causes_mean <dbl>, sup_soc_causes_sd <dbl>,
# sup_pol_causes_mean <dbl>, sup_pol_causes_sd <dbl>, …Anova per each variable
# --- ANOVA per each variable ---
anova_results <- sapply(num_vars, function(v) {
if (all(is.na(data_clustered[[v]]))) return(NA)
fit <- aov(data_clustered[[v]] ~ data_clustered$clust)
summary(fit)[[1]][["Pr(>F)"]][1]
})
anova_results <- sort(na.omit(anova_results))
cat("\n--- ANOVA p-values (quantitative vars) ---\n")
--- ANOVA p-values (quantitative vars) ---print(anova_results) Twitter_X Facebook Linkedin Instagram
1.852602e-185 1.426057e-149 1.827589e-149 2.398475e-139
TikTok Telegram WhatsApp YouTube
9.243475e-138 1.427796e-136 6.313985e-90 3.765550e-66
sup_soc_causes sup_pol_causes inf_socially inf_pol
3.301317e-31 5.920672e-28 1.824587e-27 1.075924e-26
Partisan_mobil Info_Online Info_Offline Civic_Engagement
2.391023e-22 3.984051e-16 4.431335e-13 5.259269e-05
Protest_Action
3.739654e-01 Summarize of categorical variables per cluster
# 12.5 Summarize of categorical variables per cluster --------------
cat("\n===== Categorical Variables by Cluster =====\n")
===== Categorical Variables by Cluster =====for (v in cat_vars) {
cat("\n###", v, "###\n")
tab <- table(data_clustered[[v]], data_clustered$clust)
print(tab)
test <- suppressWarnings(chisq.test(tab))
print(test)
}
### Age_cat ###
1 2 3
0_17 0 0 0
18_24 8 46 46
25_34 20 71 66
35_44 18 101 70
45_54 40 145 66
55_64 48 144 39
65_+ 145 183 45
Pearson's Chi-squared test
data: tab
X-squared = NaN, df = 12, p-value = NA
### Vote ###
1 2 3
+Europa 11 16 12
Alleanza Verdi-Sinistra 11 32 23
Azione-Italia Viva 24 41 26
Did not vote 39 95 39
Forza Italia 7 20 17
Fratelli d'Italia 37 90 39
Lega 11 37 11
Movimento 5 Stelle 24 93 50
Other party 21 27 29
Partito Democratico 64 166 54
Pearson's Chi-squared test
data: tab
X-squared = 37.656, df = 18, p-value = 0.0043
### Sex ###
1 2 3
Male 165 285 174
Female 113 403 156
Non-binary 1 2 2
Pearson's Chi-squared test
data: tab
X-squared = 29.797, df = 4, p-value = 5.384e-06
### Left_Right ###
1 2 3
Ext_left 28 91 26
Left 64 162 55
Center 67 162 92
Right 30 87 56
Ext_right 23 64 40
No_position 58 112 50
Pearson's Chi-squared test
data: tab
X-squared = 23.897, df = 10, p-value = 0.007879
### Pol_int ###
1 2 3
Very interested 46 142 73
Somewhat interested 141 292 172
Not very interested 61 198 62
Not at all interested 31 58 25
Pearson's Chi-squared test
data: tab
X-squared = 20.757, df = 6, p-value = 0.002028
### Inf_disagr ###
1 2 3
Very often 24 61 65
Often 71 225 129
sometimes 97 242 106
Rarely 47 95 21
Never 40 67 11
Pearson's Chi-squared test
data: tab
X-squared = 70.25, df = 8, p-value = 4.383e-12
### Inf_check ###
1 2 3
Very often 28 69 75
Often 81 224 133
sometimes 88 244 95
Rarely 45 91 20
Never 37 62 9
Pearson's Chi-squared test
data: tab
X-squared = 74.82, df = 8, p-value = 5.359e-13
### Inf_confirm ###
1 2 3
Very often 32 84 72
Often 89 256 132
sometimes 74 216 99
Rarely 42 73 18
Never 42 61 11
Pearson's Chi-squared test
data: tab
X-squared = 58.171, df = 8, p-value = 1.064e-09
### clust ###
1 2 3
1 279 0 0
2 0 690 0
3 0 0 332
Pearson's Chi-squared test
data: tab
X-squared = 2602, df = 4, p-value < 2.2e-16# 12.6 Visualization: boxplot for most significant variables----------
top_num <- names(head(anova_results, 3))
for (v in top_num) {
p <- ggplot(data_clustered, aes(x = clust, y = .data[[v]], fill = clust)) +
geom_boxplot(alpha = 0.7) +
theme_minimal() +
labs(title = paste("Distribution of", v, "by Cluster"),
x = "Cluster", y = v) +
theme(legend.position = "none") +
scale_fill_brewer(palette = "Dark2")
print(p)
}Warning: Removed 69 rows containing non-finite outside the scale range
(`stat_boxplot()`).
Warning: Removed 13 rows containing non-finite outside the scale range
(`stat_boxplot()`).
Warning: Removed 63 rows containing non-finite outside the scale range
(`stat_boxplot()`).
# 12.7 HCPC Internal Descriptions -----------------------
cat("\n===== HCPC Internal Descriptions =====\n")
===== HCPC Internal Descriptions =====cat("\n--- Quantitative Variables ---\n")
--- Quantitative Variables ---print(hcpc_res$desc.var$quanti)$`1`
v.test Mean in category Overall mean sd in category
inf_pol -8.619589 0.4444444 0.6610300 0.4969040
sup_pol_causes -8.638983 0.3046595 0.5334358 0.4602631
Twitter_X -8.647238 1.5208886 2.3879870 1.2570367
sup_soc_causes -9.494062 0.3118280 0.5618755 0.4632400
Linkedin -9.557636 1.4937609 2.3449111 1.0114933
inf_socially -10.029012 0.5017921 0.7363566 0.4999968
TikTok -10.676741 1.3567245 2.5052293 1.0014777
Telegram -11.251364 1.7333518 2.9435939 1.3722745
YouTube -11.756496 3.0788530 4.1284047 1.6872548
WhatsApp -18.754809 4.5585063 5.8232558 1.9894849
Instagram -20.772240 1.6525179 4.0525078 1.3191793
Facebook -23.084470 2.2688172 4.6645963 1.7748125
Overall sd p.value
inf_pol 0.4733596 6.719496e-18
sup_pol_causes 0.4988808 5.671564e-18
Twitter_X 1.8890319 5.276066e-18
sup_soc_causes 0.4961567 2.222026e-21
Linkedin 1.6776605 1.204775e-21
inf_socially 0.4406081 1.136478e-23
TikTok 2.0264803 1.307819e-26
Telegram 2.0263543 2.280375e-29
YouTube 1.6817996 6.539233e-32
WhatsApp 1.2703992 1.768531e-78
Instagram 2.1765789 7.717460e-96
Facebook 1.9551280 6.631616e-118
$`2`
v.test Mean in category Overall mean sd in category Overall sd
WhatsApp 11.460297 6.2032356 5.8232558 0.5104873 1.2703992
Facebook 11.325052 5.2424791 4.6645963 1.4653822 1.9551280
Instagram 4.132437 4.2872575 4.0525078 2.0144862 2.1765789
inf_socially 2.131661 0.7608696 0.7363566 0.4265525 0.4406081
YouTube -3.064133 3.9939097 4.1284047 1.5427273 1.6817996
Telegram -9.914354 2.4192645 2.9435939 1.7175343 2.0263543
TikTok -10.546046 1.9474576 2.5052293 1.5944161 2.0264803
Linkedin -12.174441 1.8118494 2.3449111 1.1343184 1.6776605
Twitter_X -14.666328 1.6649092 2.3879870 1.1924886 1.8890319
p.value
WhatsApp 2.087968e-30
Facebook 9.862073e-30
Instagram 3.589379e-05
inf_socially 3.303475e-02
YouTube 2.183022e-03
Telegram 3.605820e-23
TikTok 5.298021e-26
Linkedin 4.252967e-34
Twitter_X 1.059250e-48
$`3`
v.test Mean in category Overall mean sd in category
Twitter_X 24.930347 4.6194453 2.3879870 1.7327380
Linkedin 22.934829 4.1680543 2.3449111 1.7239945
TikTok 22.124288 4.6296126 2.5052293 1.9230664
Telegram 21.942131 5.0503553 2.9435939 1.4616608
Instagram 14.825351 5.5814835 4.0525078 1.1727849
YouTube 14.575831 5.2899301 4.1284047 1.1968981
sup_soc_causes 9.155220 0.7771084 0.5618755 0.4161862
sup_pol_causes 9.034122 0.7469880 0.5334358 0.4347378
Facebook 8.768383 5.4768952 4.6645963 1.3007396
inf_pol 8.666931 0.8554217 0.6610300 0.3516752
inf_socially 7.001561 0.8825301 0.7363566 0.3219794
WhatsApp 4.537405 6.0963855 5.8232558 0.8588933
Overall sd p.value
Twitter_X 1.8890319 3.488870e-137
Linkedin 1.6776605 2.088283e-116
TikTok 2.0264803 1.845264e-108
Telegram 2.0263543 1.029628e-106
Instagram 2.1765789 1.004540e-49
YouTube 1.6817996 4.002215e-48
sup_soc_causes 0.4961567 5.424727e-20
sup_pol_causes 0.4988808 1.653240e-19
Facebook 1.9551280 1.812509e-18
inf_pol 0.4733596 4.439233e-18
inf_socially 0.4406081 2.531263e-12
WhatsApp 1.2703992 5.695056e-06cat("\n--- Qualitative Variables ---\n")
--- Qualitative Variables ---print(hcpc_res$desc.var$quali)NULLAnova Analysis
Anova platform use (DV) per sociopolitical variables
# 13. ANOVA: platform use x age, ideological...
# Platform use
social_vars <- c("YouTube", "WhatsApp", "Twitter_X", "Instagram",
"TikTok", "Facebook", "Linkedin", "Telegram")
# 13.1 Platform and ideology (Left_Right) ---------
# Descriptives
desc_stats <- data_PCA3 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(social_vars),
names_to = "Platform",
values_to = "Frequency"
) %>%
dplyr::group_by(Platform, Left_Right) %>%
dplyr::summarise(
n = dplyr::n(),
mean = mean(Frequency, na.rm = TRUE),
sd = sd(Frequency, na.rm = TRUE),
se = sd / sqrt(n),
.groups = "drop"
)
knitr::kable(
desc_stats,
digits = 2,
caption = "Descriptives per platform & ideology"
)| Platform | Left_Right | n | mean | sd | se |
|---|---|---|---|---|---|
| Ext_left | 145 | 4.65 | 1.88 | 0.16 | |
| Left | 281 | 4.51 | 1.94 | 0.12 | |
| Center | 321 | 4.71 | 1.96 | 0.11 | |
| Right | 173 | 4.83 | 1.86 | 0.14 | |
| Ext_right | 127 | 5.11 | 2.00 | 0.18 | |
| No_position | 220 | 4.41 | 2.08 | 0.14 | |
| NA | 34 | 4.77 | 1.99 | 0.34 | |
| Ext_left | 145 | 4.06 | 2.28 | 0.19 | |
| Left | 281 | 3.91 | 2.23 | 0.13 | |
| Center | 321 | 4.22 | 2.11 | 0.12 | |
| Right | 173 | 4.20 | 2.16 | 0.16 | |
| Ext_right | 127 | 4.15 | 2.28 | 0.20 | |
| No_position | 220 | 3.75 | 2.20 | 0.15 | |
| NA | 34 | 4.52 | 2.19 | 0.38 | |
| Ext_left | 145 | 2.02 | 1.63 | 0.14 | |
| Left | 281 | 2.17 | 1.55 | 0.09 | |
| Center | 321 | 2.50 | 1.75 | 0.10 | |
| Right | 173 | 2.59 | 1.83 | 0.14 | |
| Ext_right | 127 | 2.47 | 1.91 | 0.17 | |
| No_position | 220 | 2.21 | 1.65 | 0.11 | |
| NA | 34 | 2.93 | 1.96 | 0.34 | |
| Telegram | Ext_left | 145 | 2.35 | 1.82 | 0.15 |
| Telegram | Left | 281 | 2.74 | 2.00 | 0.12 |
| Telegram | Center | 321 | 2.95 | 2.04 | 0.11 |
| Telegram | Right | 173 | 3.27 | 2.07 | 0.16 |
| Telegram | Ext_right | 127 | 3.43 | 2.34 | 0.21 |
| Telegram | No_position | 220 | 2.98 | 2.12 | 0.14 |
| Telegram | NA | 34 | 3.52 | 2.08 | 0.36 |
| TikTok | Ext_left | 145 | 2.36 | 2.08 | 0.17 |
| TikTok | Left | 281 | 2.07 | 1.80 | 0.11 |
| TikTok | Center | 321 | 2.41 | 2.02 | 0.11 |
| TikTok | Right | 173 | 2.90 | 2.14 | 0.16 |
| TikTok | Ext_right | 127 | 3.15 | 2.32 | 0.21 |
| TikTok | No_position | 220 | 2.48 | 2.08 | 0.14 |
| TikTok | NA | 34 | 3.48 | 2.32 | 0.40 |
| Twitter_X | Ext_left | 145 | 2.22 | 1.83 | 0.15 |
| Twitter_X | Left | 281 | 2.18 | 1.79 | 0.11 |
| Twitter_X | Center | 321 | 2.57 | 2.02 | 0.11 |
| Twitter_X | Right | 173 | 2.64 | 2.06 | 0.16 |
| Twitter_X | Ext_right | 127 | 2.76 | 2.21 | 0.20 |
| Twitter_X | No_position | 220 | 2.09 | 1.77 | 0.12 |
| Twitter_X | NA | 34 | 2.41 | 1.89 | 0.32 |
| Ext_left | 145 | 5.93 | 1.21 | 0.10 | |
| Left | 281 | 5.89 | 1.15 | 0.07 | |
| Center | 321 | 5.87 | 1.23 | 0.07 | |
| Right | 173 | 5.81 | 1.23 | 0.09 | |
| Ext_right | 127 | 6.05 | 1.17 | 0.10 | |
| No_position | 220 | 5.53 | 1.55 | 0.10 | |
| NA | 34 | 5.50 | 1.48 | 0.25 | |
| YouTube | Ext_left | 145 | 4.01 | 1.54 | 0.13 |
| YouTube | Left | 281 | 3.91 | 1.63 | 0.10 |
| YouTube | Center | 321 | 4.22 | 1.71 | 0.10 |
| YouTube | Right | 173 | 4.17 | 1.65 | 0.13 |
| YouTube | Ext_right | 127 | 4.44 | 1.75 | 0.16 |
| YouTube | No_position | 220 | 4.08 | 1.80 | 0.12 |
| YouTube | NA | 34 | 4.60 | 1.73 | 0.30 |
# Welch ANOVA + eta squared per each platform
anova_results <- data_PCA3 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(social_vars),
names_to = "Platform",
values_to = "Frequency"
) %>%
dplyr::group_by(Platform) %>%
dplyr::summarise(
Welch_p = oneway.test(Frequency ~ Left_Right,
data = dplyr::cur_data(),
var.equal = FALSE)$p.value,
eta_sq = {
eta_res <- rstatix::eta_squared(aov(Frequency ~ Left_Right,
data = dplyr::cur_data()))
if (is.data.frame(eta_res)) eta_res$Eta2[1] else as.numeric(eta_res[1])
},
.groups = "drop"
)Warning: There was 1 warning in `dplyr::summarise()`.
ℹ In argument: `Welch_p = `$`(...)`.
ℹ In group 1: `Platform = "Facebook"`.
Caused by warning:
! `cur_data()` was deprecated in dplyr 1.1.0.
ℹ Please use `pick()` instead.knitr::kable(
anova_results,
digits = 3,
caption = "Welch ANOVA with eta-squared (per ideology)"
)| Platform | Welch_p | eta_sq |
|---|---|---|
| 0.027 | 0.011 | |
| 0.142 | 0.007 | |
| 0.009 | 0.013 | |
| Telegram | 0.000 | 0.020 |
| TikTok | 0.000 | 0.026 |
| Twitter_X | 0.003 | 0.015 |
| 0.017 | 0.014 | |
| YouTube | 0.057 | 0.009 |
# Games–Howell post-hoc per each platform
gh_results <- data_PCA3 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(social_vars),
names_to = "Platform",
values_to = "Frequency"
) %>%
dplyr::group_by(Platform) %>%
rstatix::games_howell_test(Frequency ~ Left_Right) %>%
dplyr::mutate(
p_sig = dplyr::case_when(
p.adj < .001 ~ "***",
p.adj < .01 ~ "**",
p.adj < .05 ~ "*",
TRUE ~ ""
)
) %>%
dplyr::select(
Platform, group1, group2,
mean_difference = estimate, p.adj, p_sig
)
knitr::kable(
gh_results,
digits = 3,
caption = "Comparison post-hoc Games–Howell (per ideology)"
)| Platform | group1 | group2 | mean_difference | p.adj | p_sig |
|---|---|---|---|---|---|
| Ext_left | Left | -0.139 | 0.981 | ||
| Ext_left | Center | 0.060 | 1.000 | ||
| Ext_left | Right | 0.187 | 0.951 | ||
| Ext_left | Ext_right | 0.466 | 0.369 | ||
| Ext_left | No_position | -0.234 | 0.879 | ||
| Left | Center | 0.199 | 0.813 | ||
| Left | Right | 0.325 | 0.484 | ||
| Left | Ext_right | 0.605 | 0.056 | ||
| Left | No_position | -0.095 | 0.995 | ||
| Center | Right | 0.126 | 0.981 | ||
| Center | Ext_right | 0.406 | 0.384 | ||
| Center | No_position | -0.294 | 0.569 | ||
| Right | Ext_right | 0.280 | 0.824 | ||
| Right | No_position | -0.420 | 0.290 | ||
| Ext_right | No_position | -0.700 | 0.028 | * | |
| Ext_left | Left | -0.150 | 0.988 | ||
| Ext_left | Center | 0.163 | 0.979 | ||
| Ext_left | Right | 0.145 | 0.993 | ||
| Ext_left | Ext_right | 0.096 | 0.999 | ||
| Ext_left | No_position | -0.311 | 0.799 | ||
| Left | Center | 0.313 | 0.495 | ||
| Left | Right | 0.295 | 0.738 | ||
| Left | Ext_right | 0.246 | 0.915 | ||
| Left | No_position | -0.161 | 0.967 | ||
| Center | Right | -0.018 | 1.000 | ||
| Center | Ext_right | -0.067 | 1.000 | ||
| Center | No_position | -0.474 | 0.135 | ||
| Right | Ext_right | -0.049 | 1.000 | ||
| Right | No_position | -0.456 | 0.328 | ||
| Ext_right | No_position | -0.407 | 0.597 | ||
| Ext_left | Left | 0.153 | 0.941 | ||
| Ext_left | Center | 0.480 | 0.055 | ||
| Ext_left | Right | 0.567 | 0.051 | ||
| Ext_left | Ext_right | 0.445 | 0.342 | ||
| Ext_left | No_position | 0.185 | 0.908 | ||
| Left | Center | 0.327 | 0.164 | ||
| Left | Right | 0.413 | 0.154 | ||
| Left | Ext_right | 0.292 | 0.683 | ||
| Left | No_position | 0.031 | 1.000 | ||
| Center | Right | 0.086 | 0.996 | ||
| Center | Ext_right | -0.035 | 1.000 | ||
| Center | No_position | -0.296 | 0.377 | ||
| Right | Ext_right | -0.121 | 0.995 | ||
| Right | No_position | -0.382 | 0.297 | ||
| Ext_right | No_position | -0.261 | 0.812 | ||
| Telegram | Ext_left | Left | 0.391 | 0.351 | |
| Telegram | Ext_left | Center | 0.605 | 0.023 | * |
| Telegram | Ext_left | Right | 0.923 | 0.001 | *** |
| Telegram | Ext_left | Ext_right | 1.077 | 0.001 | *** |
| Telegram | Ext_left | No_position | 0.626 | 0.040 | * |
| Telegram | Left | Center | 0.214 | 0.801 | |
| Telegram | Left | Right | 0.532 | 0.093 | |
| Telegram | Left | Ext_right | 0.687 | 0.067 | |
| Telegram | Left | No_position | 0.236 | 0.818 | |
| Telegram | Center | Right | 0.318 | 0.599 | |
| Telegram | Center | Ext_right | 0.473 | 0.388 | |
| Telegram | Center | No_position | 0.022 | 1.000 | |
| Telegram | Right | Ext_right | 0.155 | 0.993 | |
| Telegram | Right | No_position | -0.296 | 0.750 | |
| Telegram | Ext_right | No_position | -0.451 | 0.513 | |
| TikTok | Ext_left | Left | -0.288 | 0.731 | |
| TikTok | Ext_left | Center | 0.048 | 1.000 | |
| TikTok | Ext_left | Right | 0.539 | 0.225 | |
| TikTok | Ext_left | Ext_right | 0.787 | 0.048 | * |
| TikTok | Ext_left | No_position | 0.119 | 0.995 | |
| TikTok | Left | Center | 0.336 | 0.280 | |
| TikTok | Left | Right | 0.827 | 0.001 | *** |
| TikTok | Left | Ext_right | 1.075 | 0.000 | *** |
| TikTok | Left | No_position | 0.407 | 0.228 | |
| TikTok | Center | Right | 0.491 | 0.147 | |
| TikTok | Center | Ext_right | 0.739 | 0.026 | * |
| TikTok | Center | No_position | 0.071 | 0.999 | |
| TikTok | Right | Ext_right | 0.248 | 0.938 | |
| TikTok | Right | No_position | -0.420 | 0.402 | |
| TikTok | Ext_right | No_position | -0.669 | 0.096 | |
| Twitter_X | Ext_left | Left | -0.037 | 1.000 | |
| Twitter_X | Ext_left | Center | 0.351 | 0.445 | |
| Twitter_X | Ext_left | Right | 0.417 | 0.421 | |
| Twitter_X | Ext_left | Ext_right | 0.542 | 0.279 | |
| Twitter_X | Ext_left | No_position | -0.130 | 0.986 | |
| Twitter_X | Left | Center | 0.388 | 0.141 | |
| Twitter_X | Left | Right | 0.454 | 0.187 | |
| Twitter_X | Left | Ext_right | 0.579 | 0.130 | |
| Twitter_X | Left | No_position | -0.093 | 0.993 | |
| Twitter_X | Center | Right | 0.067 | 0.999 | |
| Twitter_X | Center | Ext_right | 0.192 | 0.964 | |
| Twitter_X | Center | No_position | -0.480 | 0.052 | |
| Twitter_X | Right | Ext_right | 0.125 | 0.997 | |
| Twitter_X | Right | No_position | -0.547 | 0.081 | |
| Twitter_X | Ext_right | No_position | -0.672 | 0.060 | |
| Ext_left | Left | -0.045 | 0.999 | ||
| Ext_left | Center | -0.063 | 0.996 | ||
| Ext_left | Right | -0.123 | 0.947 | ||
| Ext_left | Ext_right | 0.117 | 0.966 | ||
| Ext_left | No_position | -0.401 | 0.067 | ||
| Left | Center | -0.018 | 1.000 | ||
| Left | Right | -0.078 | 0.985 | ||
| Left | Ext_right | 0.161 | 0.786 | ||
| Left | No_position | -0.356 | 0.054 | ||
| Center | Right | -0.060 | 0.995 | ||
| Center | Ext_right | 0.179 | 0.702 | ||
| Center | No_position | -0.338 | 0.079 | ||
| Right | Ext_right | 0.239 | 0.523 | ||
| Right | No_position | -0.278 | 0.358 | ||
| Ext_right | No_position | -0.518 | 0.007 | ** | |
| YouTube | Ext_left | Left | -0.097 | 0.991 | |
| YouTube | Ext_left | Center | 0.217 | 0.753 | |
| YouTube | Ext_left | Right | 0.162 | 0.946 | |
| YouTube | Ext_left | Ext_right | 0.432 | 0.278 | |
| YouTube | Ext_left | No_position | 0.071 | 0.999 | |
| YouTube | Left | Center | 0.314 | 0.200 | |
| YouTube | Left | Right | 0.258 | 0.586 | |
| YouTube | Left | Ext_right | 0.529 | 0.054 | |
| YouTube | Left | No_position | 0.167 | 0.893 | |
| YouTube | Center | Right | -0.055 | 0.999 | |
| YouTube | Center | Ext_right | 0.215 | 0.853 | |
| YouTube | Center | No_position | -0.146 | 0.935 | |
| YouTube | Right | Ext_right | 0.270 | 0.763 | |
| YouTube | Right | No_position | -0.091 | 0.995 | |
| YouTube | Ext_right | No_position | -0.361 | 0.460 |
# Grafico: avg
ggplot(desc_stats,
aes(x = Left_Right, y = mean, fill = Left_Right)) +
geom_col(position = "dodge", color = "black") +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se),
width = 0.2,
position = position_dodge(0.9)) +
facet_wrap(~ Platform, scales = "free_y") +
labs(
title = "Platform use frequency and ideology",
x = "Ideology Left-Right",
y = "Use frequency (avg ±SE)"
) +
scale_fill_viridis_d() +
theme_minimal() +
theme(
legend.position = "none",
axis.text.x = element_text(angle = 90, hjust = 1)
)
# 13.2 Platforms & vote in general elections --------------------
# Descriptives platforms & vote
desc_stats_vote <- data_PCA3 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(social_vars),
names_to = "Platform",
values_to = "Frequency"
) %>%
dplyr::group_by(Platform, Vote) %>%
dplyr::summarise(
n = dplyr::n(),
mean = mean(Frequency, na.rm = TRUE),
sd = sd(Frequency, na.rm = TRUE),
se = sd / sqrt(n),
.groups = "drop"
)
knitr::kable(
desc_stats_vote,
digits = 2,
caption = "Descriptives of platforms & vote in general elections"
)| Platform | Vote | n | mean | sd | se |
|---|---|---|---|---|---|
| +Europa | 39 | 3.85 | 2.01 | 0.32 | |
| Alleanza Verdi-Sinistra | 66 | 4.70 | 1.86 | 0.23 | |
| Azione-Italia Viva | 91 | 4.20 | 2.14 | 0.22 | |
| Did not vote | 173 | 4.37 | 1.94 | 0.15 | |
| Forza Italia | 44 | 5.20 | 1.95 | 0.29 | |
| Fratelli d’Italia | 166 | 4.79 | 1.95 | 0.15 | |
| Lega | 59 | 5.16 | 2.01 | 0.26 | |
| Movimento 5 Stelle | 167 | 5.03 | 1.79 | 0.14 | |
| Other party | 77 | 4.38 | 2.16 | 0.25 | |
| Partito Democratico | 284 | 4.72 | 1.92 | 0.11 | |
| NA | 135 | 4.62 | 1.96 | 0.17 | |
| +Europa | 39 | 4.15 | 2.23 | 0.36 | |
| Alleanza Verdi-Sinistra | 66 | 4.52 | 1.97 | 0.24 | |
| Azione-Italia Viva | 91 | 4.20 | 2.21 | 0.23 | |
| Did not vote | 173 | 3.87 | 2.15 | 0.16 | |
| Forza Italia | 44 | 5.02 | 1.85 | 0.28 | |
| Fratelli d’Italia | 166 | 3.93 | 2.25 | 0.17 | |
| Lega | 59 | 4.12 | 2.23 | 0.29 | |
| Movimento 5 Stelle | 167 | 4.28 | 2.20 | 0.17 | |
| Other party | 77 | 3.76 | 2.19 | 0.25 | |
| Partito Democratico | 284 | 3.79 | 2.27 | 0.13 | |
| NA | 135 | 4.17 | 2.13 | 0.18 | |
| +Europa | 39 | 2.66 | 1.82 | 0.29 | |
| Alleanza Verdi-Sinistra | 66 | 2.86 | 1.84 | 0.23 | |
| Azione-Italia Viva | 91 | 2.44 | 1.70 | 0.18 | |
| Did not vote | 173 | 2.31 | 1.71 | 0.13 | |
| Forza Italia | 44 | 2.86 | 1.86 | 0.28 | |
| Fratelli d’Italia | 166 | 1.96 | 1.49 | 0.12 | |
| Lega | 59 | 2.12 | 1.58 | 0.21 | |
| Movimento 5 Stelle | 167 | 2.46 | 1.86 | 0.14 | |
| Other party | 77 | 2.61 | 1.84 | 0.21 | |
| Partito Democratico | 284 | 2.25 | 1.67 | 0.10 | |
| NA | 135 | 2.27 | 1.68 | 0.14 | |
| Telegram | +Europa | 39 | 2.95 | 1.93 | 0.31 |
| Telegram | Alleanza Verdi-Sinistra | 66 | 3.23 | 2.29 | 0.28 |
| Telegram | Azione-Italia Viva | 91 | 2.63 | 2.04 | 0.21 |
| Telegram | Did not vote | 173 | 2.98 | 2.06 | 0.16 |
| Telegram | Forza Italia | 44 | 3.60 | 2.07 | 0.31 |
| Telegram | Fratelli d’Italia | 166 | 3.12 | 2.18 | 0.17 |
| Telegram | Lega | 59 | 2.87 | 2.08 | 0.27 |
| Telegram | Movimento 5 Stelle | 167 | 3.03 | 2.15 | 0.17 |
| Telegram | Other party | 77 | 3.42 | 1.93 | 0.22 |
| Telegram | Partito Democratico | 284 | 2.57 | 1.95 | 0.12 |
| Telegram | NA | 135 | 3.01 | 2.08 | 0.18 |
| TikTok | +Europa | 39 | 3.13 | 2.36 | 0.38 |
| TikTok | Alleanza Verdi-Sinistra | 66 | 2.62 | 2.21 | 0.27 |
| TikTok | Azione-Italia Viva | 91 | 2.11 | 1.79 | 0.19 |
| TikTok | Did not vote | 173 | 2.58 | 2.05 | 0.16 |
| TikTok | Forza Italia | 44 | 3.57 | 2.50 | 0.38 |
| TikTok | Fratelli d’Italia | 166 | 2.49 | 2.00 | 0.16 |
| TikTok | Lega | 59 | 2.46 | 2.06 | 0.27 |
| TikTok | Movimento 5 Stelle | 167 | 2.96 | 2.26 | 0.17 |
| TikTok | Other party | 77 | 3.01 | 2.17 | 0.25 |
| TikTok | Partito Democratico | 284 | 2.05 | 1.88 | 0.11 |
| TikTok | NA | 135 | 2.26 | 1.88 | 0.16 |
| Twitter_X | +Europa | 39 | 2.39 | 1.88 | 0.30 |
| Twitter_X | Alleanza Verdi-Sinistra | 66 | 2.75 | 1.98 | 0.24 |
| Twitter_X | Azione-Italia Viva | 91 | 2.74 | 1.97 | 0.21 |
| Twitter_X | Did not vote | 173 | 2.37 | 1.89 | 0.14 |
| Twitter_X | Forza Italia | 44 | 3.05 | 2.33 | 0.35 |
| Twitter_X | Fratelli d’Italia | 166 | 2.29 | 1.89 | 0.15 |
| Twitter_X | Lega | 59 | 2.21 | 2.04 | 0.27 |
| Twitter_X | Movimento 5 Stelle | 167 | 2.63 | 2.07 | 0.16 |
| Twitter_X | Other party | 77 | 2.93 | 2.23 | 0.25 |
| Twitter_X | Partito Democratico | 284 | 2.12 | 1.82 | 0.11 |
| Twitter_X | NA | 135 | 1.93 | 1.60 | 0.14 |
| +Europa | 39 | 6.00 | 1.38 | 0.22 | |
| Alleanza Verdi-Sinistra | 66 | 5.86 | 1.12 | 0.14 | |
| Azione-Italia Viva | 91 | 5.69 | 1.47 | 0.15 | |
| Did not vote | 173 | 5.60 | 1.51 | 0.11 | |
| Forza Italia | 44 | 5.93 | 1.30 | 0.20 | |
| Fratelli d’Italia | 166 | 5.92 | 1.12 | 0.09 | |
| Lega | 59 | 5.90 | 1.28 | 0.17 | |
| Movimento 5 Stelle | 167 | 5.89 | 1.16 | 0.09 | |
| Other party | 77 | 5.71 | 1.36 | 0.16 | |
| Partito Democratico | 284 | 5.86 | 1.19 | 0.07 | |
| NA | 135 | 5.82 | 1.30 | 0.11 | |
| YouTube | +Europa | 39 | 3.69 | 1.36 | 0.22 |
| YouTube | Alleanza Verdi-Sinistra | 66 | 4.30 | 1.48 | 0.18 |
| YouTube | Azione-Italia Viva | 91 | 4.24 | 1.74 | 0.18 |
| YouTube | Did not vote | 173 | 4.17 | 1.72 | 0.13 |
| YouTube | Forza Italia | 44 | 4.82 | 1.54 | 0.23 |
| YouTube | Fratelli d’Italia | 166 | 4.30 | 1.70 | 0.13 |
| YouTube | Lega | 59 | 4.09 | 1.83 | 0.24 |
| YouTube | Movimento 5 Stelle | 167 | 4.40 | 1.55 | 0.12 |
| YouTube | Other party | 77 | 4.25 | 1.77 | 0.20 |
| YouTube | Partito Democratico | 284 | 3.75 | 1.69 | 0.10 |
| YouTube | NA | 135 | 4.01 | 1.76 | 0.15 |
# Welch ANOVA + eta squared per vote
anova_results_vote <- data_PCA3 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(social_vars),
names_to = "Platform",
values_to = "Frequency"
) %>%
dplyr::group_by(Platform) %>%
dplyr::summarise(
Welch_p = oneway.test(Frequency ~ Vote,
data = dplyr::cur_data(),
var.equal = FALSE)$p.value,
eta_sq = {
eta_res <- rstatix::eta_squared(aov(Frequency ~ Vote,
data = dplyr::cur_data()))
if (is.data.frame(eta_res)) eta_res$Eta2[1] else as.numeric(eta_res[1])
},
.groups = "drop"
)
knitr::kable(
anova_results_vote,
digits = 3,
caption = "Welch ANOVA con eta-squared (per party voted)"
)| Platform | Welch_p | eta_sq |
|---|---|---|
| 0.001 | 0.027 | |
| 0.005 | 0.018 | |
| 0.007 | 0.021 | |
| Telegram | 0.008 | 0.020 |
| TikTok | 0.000 | 0.038 |
| Twitter_X | 0.014 | 0.021 |
| 0.560 | 0.008 | |
| YouTube | 0.000 | 0.028 |
# Games–Howell post-hoc per vote
gh_results_vote <- data_PCA3 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(social_vars),
names_to = "Platform",
values_to = "Frequency"
) %>%
dplyr::group_by(Platform) %>%
rstatix::games_howell_test(Frequency ~ Vote) %>%
dplyr::mutate(
p_sig = dplyr::case_when(
p.adj < .001 ~ "***",
p.adj < .01 ~ "**",
p.adj < .05 ~ "*",
TRUE ~ ""
)
) %>%
dplyr::select(
Platform, group1, group2,
mean_difference = estimate, p.adj, p_sig
)
knitr::kable(
gh_results_vote,
digits = 3,
caption = "Comparison post-hoc Games–Howell (per party voted)"
)| Platform | group1 | group2 | mean_difference | p.adj | p_sig |
|---|---|---|---|---|---|
| +Europa | Alleanza Verdi-Sinistra | 0.851 | 0.494 | ||
| +Europa | Azione-Italia Viva | 0.352 | 0.996 | ||
| +Europa | Did not vote | 0.523 | 0.896 | ||
| +Europa | Forza Italia | 1.358 | 0.072 | ||
| +Europa | Fratelli d’Italia | 0.948 | 0.213 | ||
| +Europa | Lega | 1.312 | 0.067 | ||
| +Europa | Movimento 5 Stelle | 1.184 | 0.041 | * | |
| +Europa | Other party | 0.535 | 0.946 | ||
| +Europa | Partito Democratico | 0.876 | 0.261 | ||
| Alleanza Verdi-Sinistra | Azione-Italia Viva | -0.499 | 0.865 | ||
| Alleanza Verdi-Sinistra | Did not vote | -0.328 | 0.971 | ||
| Alleanza Verdi-Sinistra | Forza Italia | 0.508 | 0.935 | ||
| Alleanza Verdi-Sinistra | Fratelli d’Italia | 0.097 | 1.000 | ||
| Alleanza Verdi-Sinistra | Lega | 0.461 | 0.948 | ||
| Alleanza Verdi-Sinistra | Movimento 5 Stelle | 0.333 | 0.963 | ||
| Alleanza Verdi-Sinistra | Other party | -0.315 | 0.995 | ||
| Alleanza Verdi-Sinistra | Partito Democratico | 0.025 | 1.000 | ||
| Azione-Italia Viva | Did not vote | 0.171 | 1.000 | ||
| Azione-Italia Viva | Forza Italia | 1.007 | 0.179 | ||
| Azione-Italia Viva | Fratelli d’Italia | 0.596 | 0.459 | ||
| Azione-Italia Viva | Lega | 0.960 | 0.161 | ||
| Azione-Italia Viva | Movimento 5 Stelle | 0.833 | 0.058 | ||
| Azione-Italia Viva | Other party | 0.184 | 1.000 | ||
| Azione-Italia Viva | Partito Democratico | 0.524 | 0.540 | ||
| Did not vote | Forza Italia | 0.835 | 0.270 | ||
| Did not vote | Fratelli d’Italia | 0.425 | 0.607 | ||
| Did not vote | Lega | 0.789 | 0.240 | ||
| Did not vote | Movimento 5 Stelle | 0.661 | 0.043 | * | |
| Did not vote | Other party | 0.013 | 1.000 | ||
| Did not vote | Partito Democratico | 0.353 | 0.687 | ||
| Forza Italia | Fratelli d’Italia | -0.411 | 0.963 | ||
| Forza Italia | Lega | -0.047 | 1.000 | ||
| Forza Italia | Movimento 5 Stelle | -0.174 | 1.000 | ||
| Forza Italia | Other party | -0.823 | 0.503 | ||
| Forza Italia | Partito Democratico | -0.483 | 0.873 | ||
| Fratelli d’Italia | Lega | 0.364 | 0.972 | ||
| Fratelli d’Italia | Movimento 5 Stelle | 0.236 | 0.979 | ||
| Fratelli d’Italia | Other party | -0.412 | 0.919 | ||
| Fratelli d’Italia | Partito Democratico | -0.072 | 1.000 | ||
| Lega | Movimento 5 Stelle | -0.128 | 1.000 | ||
| Lega | Other party | -0.776 | 0.506 | ||
| Lega | Partito Democratico | -0.436 | 0.885 | ||
| Movimento 5 Stelle | Other party | -0.649 | 0.407 | ||
| Movimento 5 Stelle | Partito Democratico | -0.308 | 0.786 | ||
| Other party | Partito Democratico | 0.340 | 0.963 | ||
| +Europa | Alleanza Verdi-Sinistra | 0.369 | 0.997 | ||
| +Europa | Azione-Italia Viva | 0.044 | 1.000 | ||
| +Europa | Did not vote | -0.280 | 0.999 | ||
| +Europa | Forza Italia | 0.869 | 0.661 | ||
| +Europa | Fratelli d’Italia | -0.227 | 1.000 | ||
| +Europa | Lega | -0.029 | 1.000 | ||
| +Europa | Movimento 5 Stelle | 0.125 | 1.000 | ||
| +Europa | Other party | -0.397 | 0.996 | ||
| +Europa | Partito Democratico | -0.368 | 0.993 | ||
| Alleanza Verdi-Sinistra | Azione-Italia Viva | -0.325 | 0.994 | ||
| Alleanza Verdi-Sinistra | Did not vote | -0.649 | 0.465 | ||
| Alleanza Verdi-Sinistra | Forza Italia | 0.500 | 0.941 | ||
| Alleanza Verdi-Sinistra | Fratelli d’Italia | -0.597 | 0.614 | ||
| Alleanza Verdi-Sinistra | Lega | -0.398 | 0.990 | ||
| Alleanza Verdi-Sinistra | Movimento 5 Stelle | -0.244 | 0.998 | ||
| Alleanza Verdi-Sinistra | Other party | -0.766 | 0.480 | ||
| Alleanza Verdi-Sinistra | Partito Democratico | -0.737 | 0.213 | ||
| Azione-Italia Viva | Did not vote | -0.324 | 0.980 | ||
| Azione-Italia Viva | Forza Italia | 0.825 | 0.422 | ||
| Azione-Italia Viva | Fratelli d’Italia | -0.271 | 0.995 | ||
| Azione-Italia Viva | Lega | -0.073 | 1.000 | ||
| Azione-Italia Viva | Movimento 5 Stelle | 0.081 | 1.000 | ||
| Azione-Italia Viva | Other party | -0.441 | 0.956 | ||
| Azione-Italia Viva | Partito Democratico | -0.412 | 0.876 | ||
| Did not vote | Forza Italia | 1.149 | 0.024 | * | |
| Did not vote | Fratelli d’Italia | 0.052 | 1.000 | ||
| Did not vote | Lega | 0.251 | 0.999 | ||
| Did not vote | Movimento 5 Stelle | 0.405 | 0.797 | ||
| Did not vote | Other party | -0.117 | 1.000 | ||
| Did not vote | Partito Democratico | -0.089 | 1.000 | ||
| Forza Italia | Fratelli d’Italia | -1.097 | 0.044 | * | |
| Forza Italia | Lega | -0.898 | 0.470 | ||
| Forza Italia | Movimento 5 Stelle | -0.744 | 0.426 | ||
| Forza Italia | Other party | -1.266 | 0.037 | * | |
| Forza Italia | Partito Democratico | -1.238 | 0.007 | ** | |
| Fratelli d’Italia | Lega | 0.199 | 1.000 | ||
| Fratelli d’Italia | Movimento 5 Stelle | 0.352 | 0.916 | ||
| Fratelli d’Italia | Other party | -0.170 | 1.000 | ||
| Fratelli d’Italia | Partito Democratico | -0.141 | 1.000 | ||
| Lega | Movimento 5 Stelle | 0.154 | 1.000 | ||
| Lega | Other party | -0.368 | 0.995 | ||
| Lega | Partito Democratico | -0.339 | 0.989 | ||
| Movimento 5 Stelle | Other party | -0.522 | 0.792 | ||
| Movimento 5 Stelle | Partito Democratico | -0.493 | 0.420 | ||
| Other party | Partito Democratico | 0.029 | 1.000 | ||
| +Europa | Alleanza Verdi-Sinistra | 0.199 | 1.000 | ||
| +Europa | Azione-Italia Viva | -0.213 | 1.000 | ||
| +Europa | Did not vote | -0.350 | 0.985 | ||
| +Europa | Forza Italia | 0.199 | 1.000 | ||
| +Europa | Fratelli d’Italia | -0.696 | 0.479 | ||
| +Europa | Lega | -0.543 | 0.897 | ||
| +Europa | Movimento 5 Stelle | -0.195 | 1.000 | ||
| +Europa | Other party | -0.047 | 1.000 | ||
| +Europa | Partito Democratico | -0.406 | 0.948 | ||
| Alleanza Verdi-Sinistra | Azione-Italia Viva | -0.413 | 0.923 | ||
| Alleanza Verdi-Sinistra | Did not vote | -0.549 | 0.570 | ||
| Alleanza Verdi-Sinistra | Forza Italia | 0.000 | 1.000 | ||
| Alleanza Verdi-Sinistra | Fratelli d’Italia | -0.895 | 0.028 | * | |
| Alleanza Verdi-Sinistra | Lega | -0.742 | 0.381 | ||
| Alleanza Verdi-Sinistra | Movimento 5 Stelle | -0.395 | 0.913 | ||
| Alleanza Verdi-Sinistra | Other party | -0.246 | 0.999 | ||
| Alleanza Verdi-Sinistra | Partito Democratico | -0.605 | 0.342 | ||
| Azione-Italia Viva | Did not vote | -0.136 | 1.000 | ||
| Azione-Italia Viva | Forza Italia | 0.413 | 0.967 | ||
| Azione-Italia Viva | Fratelli d’Italia | -0.483 | 0.434 | ||
| Azione-Italia Viva | Lega | -0.329 | 0.977 | ||
| Azione-Italia Viva | Movimento 5 Stelle | 0.018 | 1.000 | ||
| Azione-Italia Viva | Other party | 0.167 | 1.000 | ||
| Azione-Italia Viva | Partito Democratico | -0.193 | 0.995 | ||
| Did not vote | Forza Italia | 0.549 | 0.772 | ||
| Did not vote | Fratelli d’Italia | -0.346 | 0.656 | ||
| Did not vote | Lega | -0.193 | 0.999 | ||
| Did not vote | Movimento 5 Stelle | 0.154 | 0.999 | ||
| Did not vote | Other party | 0.303 | 0.974 | ||
| Did not vote | Partito Democratico | -0.056 | 1.000 | ||
| Forza Italia | Fratelli d’Italia | -0.895 | 0.134 | ||
| Forza Italia | Lega | -0.742 | 0.562 | ||
| Forza Italia | Movimento 5 Stelle | -0.395 | 0.965 | ||
| Forza Italia | Other party | -0.246 | 1.000 | ||
| Forza Italia | Partito Democratico | -0.605 | 0.607 | ||
| Fratelli d’Italia | Lega | 0.154 | 1.000 | ||
| Fratelli d’Italia | Movimento 5 Stelle | 0.501 | 0.202 | ||
| Fratelli d’Italia | Other party | 0.649 | 0.220 | ||
| Fratelli d’Italia | Partito Democratico | 0.290 | 0.699 | ||
| Lega | Movimento 5 Stelle | 0.347 | 0.948 | ||
| Lega | Other party | 0.496 | 0.842 | ||
| Lega | Partito Democratico | 0.136 | 1.000 | ||
| Movimento 5 Stelle | Other party | 0.149 | 1.000 | ||
| Movimento 5 Stelle | Partito Democratico | -0.211 | 0.975 | ||
| Other party | Partito Democratico | -0.359 | 0.889 | ||
| Telegram | +Europa | Alleanza Verdi-Sinistra | 0.287 | 1.000 | |
| Telegram | +Europa | Azione-Italia Viva | -0.318 | 0.998 | |
| Telegram | +Europa | Did not vote | 0.028 | 1.000 | |
| Telegram | +Europa | Forza Italia | 0.653 | 0.911 | |
| Telegram | +Europa | Fratelli d’Italia | 0.176 | 1.000 | |
| Telegram | +Europa | Lega | -0.077 | 1.000 | |
| Telegram | +Europa | Movimento 5 Stelle | 0.083 | 1.000 | |
| Telegram | +Europa | Other party | 0.472 | 0.966 | |
| Telegram | +Europa | Partito Democratico | -0.382 | 0.978 | |
| Telegram | Alleanza Verdi-Sinistra | Azione-Italia Viva | -0.605 | 0.801 | |
| Telegram | Alleanza Verdi-Sinistra | Did not vote | -0.259 | 0.999 | |
| Telegram | Alleanza Verdi-Sinistra | Forza Italia | 0.366 | 0.998 | |
| Telegram | Alleanza Verdi-Sinistra | Fratelli d’Italia | -0.111 | 1.000 | |
| Telegram | Alleanza Verdi-Sinistra | Lega | -0.364 | 0.996 | |
| Telegram | Alleanza Verdi-Sinistra | Movimento 5 Stelle | -0.204 | 1.000 | |
| Telegram | Alleanza Verdi-Sinistra | Other party | 0.185 | 1.000 | |
| Telegram | Alleanza Verdi-Sinistra | Partito Democratico | -0.669 | 0.493 | |
| Telegram | Azione-Italia Viva | Did not vote | 0.346 | 0.956 | |
| Telegram | Azione-Italia Viva | Forza Italia | 0.971 | 0.300 | |
| Telegram | Azione-Italia Viva | Fratelli d’Italia | 0.494 | 0.749 | |
| Telegram | Azione-Italia Viva | Lega | 0.241 | 1.000 | |
| Telegram | Azione-Italia Viva | Movimento 5 Stelle | 0.402 | 0.903 | |
| Telegram | Azione-Italia Viva | Other party | 0.790 | 0.258 | |
| Telegram | Azione-Italia Viva | Partito Democratico | -0.064 | 1.000 | |
| Telegram | Did not vote | Forza Italia | 0.625 | 0.786 | |
| Telegram | Did not vote | Fratelli d’Italia | 0.148 | 1.000 | |
| Telegram | Did not vote | Lega | -0.105 | 1.000 | |
| Telegram | Did not vote | Movimento 5 Stelle | 0.056 | 1.000 | |
| Telegram | Did not vote | Other party | 0.444 | 0.844 | |
| Telegram | Did not vote | Partito Democratico | -0.410 | 0.565 | |
| Telegram | Forza Italia | Fratelli d’Italia | -0.477 | 0.954 | |
| Telegram | Forza Italia | Lega | -0.730 | 0.801 | |
| Telegram | Forza Italia | Movimento 5 Stelle | -0.569 | 0.869 | |
| Telegram | Forza Italia | Other party | -0.181 | 1.000 | |
| Telegram | Forza Italia | Partito Democratico | -1.034 | 0.114 | |
| Telegram | Fratelli d’Italia | Lega | -0.253 | 0.999 | |
| Telegram | Fratelli d’Italia | Movimento 5 Stelle | -0.093 | 1.000 | |
| Telegram | Fratelli d’Italia | Other party | 0.296 | 0.989 | |
| Telegram | Fratelli d’Italia | Partito Democratico | -0.558 | 0.205 | |
| Telegram | Lega | Movimento 5 Stelle | 0.160 | 1.000 | |
| Telegram | Lega | Other party | 0.549 | 0.883 | |
| Telegram | Lega | Partito Democratico | -0.305 | 0.992 | |
| Telegram | Movimento 5 Stelle | Other party | 0.388 | 0.931 | |
| Telegram | Movimento 5 Stelle | Partito Democratico | -0.465 | 0.416 | |
| Telegram | Other party | Partito Democratico | -0.853 | 0.033 | * |
| TikTok | +Europa | Alleanza Verdi-Sinistra | -0.507 | 0.986 | |
| TikTok | +Europa | Azione-Italia Viva | -1.020 | 0.351 | |
| TikTok | +Europa | Did not vote | -0.556 | 0.941 | |
| TikTok | +Europa | Forza Italia | 0.440 | 0.998 | |
| TikTok | +Europa | Fratelli d’Italia | -0.638 | 0.870 | |
| TikTok | +Europa | Lega | -0.667 | 0.919 | |
| TikTok | +Europa | Movimento 5 Stelle | -0.169 | 1.000 | |
| TikTok | +Europa | Other party | -0.117 | 1.000 | |
| TikTok | +Europa | Partito Democratico | -1.084 | 0.200 | |
| TikTok | Alleanza Verdi-Sinistra | Azione-Italia Viva | -0.514 | 0.874 | |
| TikTok | Alleanza Verdi-Sinistra | Did not vote | -0.049 | 1.000 | |
| TikTok | Alleanza Verdi-Sinistra | Forza Italia | 0.946 | 0.605 | |
| TikTok | Alleanza Verdi-Sinistra | Fratelli d’Italia | -0.131 | 1.000 | |
| TikTok | Alleanza Verdi-Sinistra | Lega | -0.161 | 1.000 | |
| TikTok | Alleanza Verdi-Sinistra | Movimento 5 Stelle | 0.338 | 0.990 | |
| TikTok | Alleanza Verdi-Sinistra | Other party | 0.389 | 0.990 | |
| TikTok | Alleanza Verdi-Sinistra | Partito Democratico | -0.578 | 0.645 | |
| TikTok | Azione-Italia Viva | Did not vote | 0.465 | 0.694 | |
| TikTok | Azione-Italia Viva | Forza Italia | 1.460 | 0.036 | * |
| TikTok | Azione-Italia Viva | Fratelli d’Italia | 0.383 | 0.870 | |
| TikTok | Azione-Italia Viva | Lega | 0.353 | 0.988 | |
| TikTok | Azione-Italia Viva | Movimento 5 Stelle | 0.852 | 0.038 | * |
| TikTok | Azione-Italia Viva | Other party | 0.903 | 0.139 | |
| TikTok | Azione-Italia Viva | Partito Democratico | -0.064 | 1.000 | |
| TikTok | Did not vote | Forza Italia | 0.995 | 0.359 | |
| TikTok | Did not vote | Fratelli d’Italia | -0.082 | 1.000 | |
| TikTok | Did not vote | Lega | -0.112 | 1.000 | |
| TikTok | Did not vote | Movimento 5 Stelle | 0.387 | 0.847 | |
| TikTok | Did not vote | Other party | 0.438 | 0.914 | |
| TikTok | Did not vote | Partito Democratico | -0.529 | 0.197 | |
| TikTok | Forza Italia | Fratelli d’Italia | -1.078 | 0.250 | |
| TikTok | Forza Italia | Lega | -1.107 | 0.379 | |
| TikTok | Forza Italia | Movimento 5 Stelle | -0.609 | 0.912 | |
| TikTok | Forza Italia | Other party | -0.557 | 0.970 | |
| TikTok | Forza Italia | Partito Democratico | -1.524 | 0.014 | * |
| TikTok | Fratelli d’Italia | Lega | -0.029 | 1.000 | |
| TikTok | Fratelli d’Italia | Movimento 5 Stelle | 0.469 | 0.623 | |
| TikTok | Fratelli d’Italia | Other party | 0.520 | 0.784 | |
| TikTok | Fratelli d’Italia | Partito Democratico | -0.446 | 0.403 | |
| TikTok | Lega | Movimento 5 Stelle | 0.498 | 0.881 | |
| TikTok | Lega | Other party | 0.550 | 0.906 | |
| TikTok | Lega | Partito Democratico | -0.417 | 0.924 | |
| TikTok | Movimento 5 Stelle | Other party | 0.051 | 1.000 | |
| TikTok | Movimento 5 Stelle | Partito Democratico | -0.915 | 0.001 | *** |
| TikTok | Other party | Partito Democratico | -0.967 | 0.029 | * |
| Twitter_X | +Europa | Alleanza Verdi-Sinistra | 0.351 | 0.996 | |
| Twitter_X | +Europa | Azione-Italia Viva | 0.350 | 0.994 | |
| Twitter_X | +Europa | Did not vote | -0.024 | 1.000 | |
| Twitter_X | +Europa | Forza Italia | 0.657 | 0.934 | |
| Twitter_X | +Europa | Fratelli d’Italia | -0.104 | 1.000 | |
| Twitter_X | +Europa | Lega | -0.187 | 1.000 | |
| Twitter_X | +Europa | Movimento 5 Stelle | 0.237 | 1.000 | |
| Twitter_X | +Europa | Other party | 0.538 | 0.941 | |
| Twitter_X | +Europa | Partito Democratico | -0.278 | 0.997 | |
| Twitter_X | Alleanza Verdi-Sinistra | Azione-Italia Viva | -0.002 | 1.000 | |
| Twitter_X | Alleanza Verdi-Sinistra | Did not vote | -0.375 | 0.955 | |
| Twitter_X | Alleanza Verdi-Sinistra | Forza Italia | 0.305 | 1.000 | |
| Twitter_X | Alleanza Verdi-Sinistra | Fratelli d’Italia | -0.456 | 0.864 | |
| Twitter_X | Alleanza Verdi-Sinistra | Lega | -0.538 | 0.914 | |
| Twitter_X | Alleanza Verdi-Sinistra | Movimento 5 Stelle | -0.115 | 1.000 | |
| Twitter_X | Alleanza Verdi-Sinistra | Other party | 0.186 | 1.000 | |
| Twitter_X | Alleanza Verdi-Sinistra | Partito Democratico | -0.629 | 0.396 | |
| Twitter_X | Azione-Italia Viva | Did not vote | -0.373 | 0.907 | |
| Twitter_X | Azione-Italia Viva | Forza Italia | 0.307 | 0.999 | |
| Twitter_X | Azione-Italia Viva | Fratelli d’Italia | -0.454 | 0.755 | |
| Twitter_X | Azione-Italia Viva | Lega | -0.537 | 0.873 | |
| Twitter_X | Azione-Italia Viva | Movimento 5 Stelle | -0.113 | 1.000 | |
| Twitter_X | Azione-Italia Viva | Other party | 0.188 | 1.000 | |
| Twitter_X | Azione-Italia Viva | Partito Democratico | -0.628 | 0.195 | |
| Twitter_X | Did not vote | Forza Italia | 0.680 | 0.794 | |
| Twitter_X | Did not vote | Fratelli d’Italia | -0.081 | 1.000 | |
| Twitter_X | Did not vote | Lega | -0.164 | 1.000 | |
| Twitter_X | Did not vote | Movimento 5 Stelle | 0.260 | 0.976 | |
| Twitter_X | Did not vote | Other party | 0.561 | 0.686 | |
| Twitter_X | Did not vote | Partito Democratico | -0.254 | 0.936 | |
| Twitter_X | Forza Italia | Fratelli d’Italia | -0.761 | 0.675 | |
| Twitter_X | Forza Italia | Lega | -0.844 | 0.728 | |
| Twitter_X | Forza Italia | Movimento 5 Stelle | -0.420 | 0.989 | |
| Twitter_X | Forza Italia | Other party | -0.119 | 1.000 | |
| Twitter_X | Forza Italia | Partito Democratico | -0.934 | 0.347 | |
| Twitter_X | Fratelli d’Italia | Lega | -0.083 | 1.000 | |
| Twitter_X | Fratelli d’Italia | Movimento 5 Stelle | 0.341 | 0.879 | |
| Twitter_X | Fratelli d’Italia | Other party | 0.642 | 0.504 | |
| Twitter_X | Fratelli d’Italia | Partito Democratico | -0.174 | 0.995 | |
| Twitter_X | Lega | Movimento 5 Stelle | 0.424 | 0.950 | |
| Twitter_X | Lega | Other party | 0.725 | 0.670 | |
| Twitter_X | Lega | Partito Democratico | -0.091 | 1.000 | |
| Twitter_X | Movimento 5 Stelle | Other party | 0.301 | 0.993 | |
| Twitter_X | Movimento 5 Stelle | Partito Democratico | -0.514 | 0.216 | |
| Twitter_X | Other party | Partito Democratico | -0.816 | 0.120 | |
| +Europa | Alleanza Verdi-Sinistra | -0.138 | 1.000 | ||
| +Europa | Azione-Italia Viva | -0.311 | 0.977 | ||
| +Europa | Did not vote | -0.395 | 0.850 | ||
| +Europa | Forza Italia | -0.068 | 1.000 | ||
| +Europa | Fratelli d’Italia | -0.078 | 1.000 | ||
| +Europa | Lega | -0.103 | 1.000 | ||
| +Europa | Movimento 5 Stelle | -0.108 | 1.000 | ||
| +Europa | Other party | -0.289 | 0.986 | ||
| +Europa | Partito Democratico | -0.137 | 1.000 | ||
| Alleanza Verdi-Sinistra | Azione-Italia Viva | -0.173 | 0.998 | ||
| Alleanza Verdi-Sinistra | Did not vote | -0.257 | 0.920 | ||
| Alleanza Verdi-Sinistra | Forza Italia | 0.070 | 1.000 | ||
| Alleanza Verdi-Sinistra | Fratelli d’Italia | 0.060 | 1.000 | ||
| Alleanza Verdi-Sinistra | Lega | 0.035 | 1.000 | ||
| Alleanza Verdi-Sinistra | Movimento 5 Stelle | 0.031 | 1.000 | ||
| Alleanza Verdi-Sinistra | Other party | -0.151 | 0.999 | ||
| Alleanza Verdi-Sinistra | Partito Democratico | 0.001 | 1.000 | ||
| Azione-Italia Viva | Did not vote | -0.084 | 1.000 | ||
| Azione-Italia Viva | Forza Italia | 0.243 | 0.993 | ||
| Azione-Italia Viva | Fratelli d’Italia | 0.233 | 0.950 | ||
| Azione-Italia Viva | Lega | 0.208 | 0.996 | ||
| Azione-Italia Viva | Movimento 5 Stelle | 0.203 | 0.980 | ||
| Azione-Italia Viva | Other party | 0.022 | 1.000 | ||
| Azione-Italia Viva | Partito Democratico | 0.174 | 0.991 | ||
| Did not vote | Forza Italia | 0.327 | 0.913 | ||
| Did not vote | Fratelli d’Italia | 0.317 | 0.474 | ||
| Did not vote | Lega | 0.292 | 0.917 | ||
| Did not vote | Movimento 5 Stelle | 0.287 | 0.632 | ||
| Did not vote | Other party | 0.106 | 1.000 | ||
| Did not vote | Partito Democratico | 0.258 | 0.677 | ||
| Forza Italia | Fratelli d’Italia | -0.010 | 1.000 | ||
| Forza Italia | Lega | -0.035 | 1.000 | ||
| Forza Italia | Movimento 5 Stelle | -0.040 | 1.000 | ||
| Forza Italia | Other party | -0.221 | 0.997 | ||
| Forza Italia | Partito Democratico | -0.069 | 1.000 | ||
| Fratelli d’Italia | Lega | -0.025 | 1.000 | ||
| Fratelli d’Italia | Movimento 5 Stelle | -0.029 | 1.000 | ||
| Fratelli d’Italia | Other party | -0.211 | 0.974 | ||
| Fratelli d’Italia | Partito Democratico | -0.059 | 1.000 | ||
| Lega | Movimento 5 Stelle | -0.004 | 1.000 | ||
| Lega | Other party | -0.186 | 0.998 | ||
| Lega | Partito Democratico | -0.034 | 1.000 | ||
| Movimento 5 Stelle | Other party | -0.182 | 0.991 | ||
| Movimento 5 Stelle | Partito Democratico | -0.030 | 1.000 | ||
| Other party | Partito Democratico | 0.152 | 0.997 | ||
| YouTube | +Europa | Alleanza Verdi-Sinistra | 0.611 | 0.497 | |
| YouTube | +Europa | Azione-Italia Viva | 0.552 | 0.644 | |
| YouTube | +Europa | Did not vote | 0.474 | 0.696 | |
| YouTube | +Europa | Forza Italia | 1.126 | 0.023 | * |
| YouTube | +Europa | Fratelli d’Italia | 0.608 | 0.353 | |
| YouTube | +Europa | Lega | 0.395 | 0.969 | |
| YouTube | +Europa | Movimento 5 Stelle | 0.711 | 0.139 | |
| YouTube | +Europa | Other party | 0.558 | 0.688 | |
| YouTube | +Europa | Partito Democratico | 0.059 | 1.000 | |
| YouTube | Alleanza Verdi-Sinistra | Azione-Italia Viva | -0.059 | 1.000 | |
| YouTube | Alleanza Verdi-Sinistra | Did not vote | -0.136 | 1.000 | |
| YouTube | Alleanza Verdi-Sinistra | Forza Italia | 0.515 | 0.768 | |
| YouTube | Alleanza Verdi-Sinistra | Fratelli d’Italia | -0.002 | 1.000 | |
| YouTube | Alleanza Verdi-Sinistra | Lega | -0.215 | 0.999 | |
| YouTube | Alleanza Verdi-Sinistra | Movimento 5 Stelle | 0.101 | 1.000 | |
| YouTube | Alleanza Verdi-Sinistra | Other party | -0.053 | 1.000 | |
| YouTube | Alleanza Verdi-Sinistra | Partito Democratico | -0.551 | 0.207 | |
| YouTube | Azione-Italia Viva | Did not vote | -0.078 | 1.000 | |
| YouTube | Azione-Italia Viva | Forza Italia | 0.574 | 0.646 | |
| YouTube | Azione-Italia Viva | Fratelli d’Italia | 0.056 | 1.000 | |
| YouTube | Azione-Italia Viva | Lega | -0.157 | 1.000 | |
| YouTube | Azione-Italia Viva | Movimento 5 Stelle | 0.159 | 0.999 | |
| YouTube | Azione-Italia Viva | Other party | 0.006 | 1.000 | |
| YouTube | Azione-Italia Viva | Partito Democratico | -0.493 | 0.363 | |
| YouTube | Did not vote | Forza Italia | 0.652 | 0.324 | |
| YouTube | Did not vote | Fratelli d’Italia | 0.134 | 0.999 | |
| YouTube | Did not vote | Lega | -0.079 | 1.000 | |
| YouTube | Did not vote | Movimento 5 Stelle | 0.237 | 0.948 | |
| YouTube | Did not vote | Other party | 0.083 | 1.000 | |
| YouTube | Did not vote | Partito Democratico | -0.415 | 0.278 | |
| YouTube | Forza Italia | Fratelli d’Italia | -0.518 | 0.650 | |
| YouTube | Forza Italia | Lega | -0.730 | 0.484 | |
| YouTube | Forza Italia | Movimento 5 Stelle | -0.415 | 0.852 | |
| YouTube | Forza Italia | Other party | -0.568 | 0.710 | |
| YouTube | Forza Italia | Partito Democratico | -1.066 | 0.003 | ** |
| YouTube | Fratelli d’Italia | Lega | -0.213 | 0.999 | |
| YouTube | Fratelli d’Italia | Movimento 5 Stelle | 0.103 | 1.000 | |
| YouTube | Fratelli d’Italia | Other party | -0.051 | 1.000 | |
| YouTube | Fratelli d’Italia | Partito Democratico | -0.549 | 0.037 | * |
| YouTube | Lega | Movimento 5 Stelle | 0.316 | 0.976 | |
| YouTube | Lega | Other party | 0.162 | 1.000 | |
| YouTube | Lega | Partito Democratico | -0.336 | 0.956 | |
| YouTube | Movimento 5 Stelle | Other party | -0.154 | 1.000 | |
| YouTube | Movimento 5 Stelle | Partito Democratico | -0.652 | 0.002 | ** |
| YouTube | Other party | Partito Democratico | -0.498 | 0.467 |
# Grapth per party voted
ggplot(desc_stats_vote,
aes(x = Vote, y = mean, fill = Vote)) +
geom_col(position = "dodge", color = "black") +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se),
width = 0.2,
position = position_dodge(0.9)) +
facet_wrap(~ Platform, scales = "free_y") +
labs(
title = "Platform use frequency and party voted in general elections",
x = "Party voted",
y = "Platform use frequency (±SE)"
) +
scale_fill_viridis_d() +
theme_minimal() +
theme(
legend.position = "none",
axis.text.x = element_text(angle = 90, hjust = 1)
)
# 13.3 Platform and age (Age_cat) -------------------
# Descriptives of platforms and age categories
desc_stats_age <- data_PCA3 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(social_vars),
names_to = "Platform",
values_to = "Frequency"
) %>%
dplyr::group_by(Platform, Age_cat) %>%
dplyr::summarise(
n = dplyr::n(),
mean = mean(Frequency, na.rm = TRUE),
sd = sd(Frequency, na.rm = TRUE),
se = sd / sqrt(n),
.groups = "drop"
)
knitr::kable(
desc_stats_age,
digits = 2,
caption = "Descriptives for platforms and age categories"
)| Platform | Age_cat | n | mean | sd | se |
|---|---|---|---|---|---|
| 18_24 | 100 | 3.92 | 2.05 | 0.21 | |
| 25_34 | 157 | 4.53 | 1.79 | 0.14 | |
| 35_44 | 189 | 5.18 | 1.65 | 0.12 | |
| 45_54 | 251 | 4.93 | 1.83 | 0.12 | |
| 55_64 | 231 | 4.82 | 1.91 | 0.13 | |
| 65_+ | 373 | 4.38 | 2.18 | 0.11 | |
| 18_24 | 100 | 5.58 | 1.53 | 0.15 | |
| 25_34 | 157 | 5.23 | 1.64 | 0.13 | |
| 35_44 | 189 | 5.02 | 1.81 | 0.13 | |
| 45_54 | 251 | 4.19 | 2.13 | 0.13 | |
| 55_64 | 231 | 3.74 | 2.06 | 0.14 | |
| 65_+ | 373 | 2.71 | 2.07 | 0.11 | |
| 18_24 | 100 | 2.59 | 1.97 | 0.20 | |
| 25_34 | 157 | 3.03 | 1.84 | 0.15 | |
| 35_44 | 189 | 3.08 | 1.93 | 0.14 | |
| 45_54 | 251 | 2.37 | 1.60 | 0.10 | |
| 55_64 | 231 | 2.12 | 1.51 | 0.10 | |
| 65_+ | 373 | 1.73 | 1.40 | 0.07 | |
| Telegram | 18_24 | 100 | 3.83 | 2.06 | 0.21 |
| Telegram | 25_34 | 157 | 3.71 | 2.00 | 0.16 |
| Telegram | 35_44 | 189 | 3.67 | 2.11 | 0.15 |
| Telegram | 45_54 | 251 | 3.12 | 2.12 | 0.13 |
| Telegram | 55_64 | 231 | 2.75 | 1.93 | 0.13 |
| Telegram | 65_+ | 373 | 1.95 | 1.71 | 0.09 |
| TikTok | 18_24 | 100 | 4.23 | 2.34 | 0.23 |
| TikTok | 25_34 | 157 | 3.12 | 2.24 | 0.18 |
| TikTok | 35_44 | 189 | 2.66 | 2.08 | 0.15 |
| TikTok | 45_54 | 251 | 2.51 | 2.06 | 0.13 |
| TikTok | 55_64 | 231 | 2.11 | 1.78 | 0.12 |
| TikTok | 65_+ | 373 | 1.91 | 1.73 | 0.09 |
| Twitter_X | 18_24 | 100 | 3.18 | 2.14 | 0.21 |
| Twitter_X | 25_34 | 157 | 2.84 | 2.13 | 0.17 |
| Twitter_X | 35_44 | 189 | 2.71 | 2.09 | 0.15 |
| Twitter_X | 45_54 | 251 | 2.27 | 1.81 | 0.11 |
| Twitter_X | 55_64 | 231 | 2.17 | 1.75 | 0.12 |
| Twitter_X | 65_+ | 373 | 2.01 | 1.80 | 0.09 |
| 18_24 | 100 | 6.07 | 0.98 | 0.10 | |
| 25_34 | 157 | 5.91 | 1.08 | 0.09 | |
| 35_44 | 189 | 6.05 | 1.15 | 0.08 | |
| 45_54 | 251 | 6.01 | 0.99 | 0.06 | |
| 55_64 | 231 | 5.87 | 1.08 | 0.07 | |
| 65_+ | 373 | 5.45 | 1.65 | 0.09 | |
| YouTube | 18_24 | 100 | 4.97 | 1.53 | 0.15 |
| YouTube | 25_34 | 157 | 4.50 | 1.52 | 0.12 |
| YouTube | 35_44 | 189 | 4.58 | 1.65 | 0.12 |
| YouTube | 45_54 | 251 | 4.21 | 1.57 | 0.10 |
| YouTube | 55_64 | 231 | 3.95 | 1.60 | 0.11 |
| YouTube | 65_+ | 373 | 3.57 | 1.76 | 0.09 |
# Welch ANOVA + eta squared per età
anova_results_age <- data_PCA3 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(social_vars),
names_to = "Platform",
values_to = "Frequency"
) %>%
dplyr::group_by(Platform) %>%
dplyr::summarise(
Welch_p = oneway.test(Frequency ~ Age_cat,
data = dplyr::cur_data(),
var.equal = FALSE)$p.value,
eta_sq = {
eta_res <- rstatix::eta_squared(aov(Frequency ~ Age_cat,
data = dplyr::cur_data()))
if (is.data.frame(eta_res)) eta_res$Eta2[1] else as.numeric(eta_res[1])
},
.groups = "drop"
)
knitr::kable(
anova_results_age,
digits = 3,
caption = "Welch ANOVA con eta-squared (per age class)"
)| Platform | Welch_p | eta_sq |
|---|---|---|
| 0 | 0.032 | |
| 0 | 0.210 | |
| 0 | 0.086 | |
| Telegram | 0 | 0.115 |
| TikTok | 0 | 0.097 |
| Twitter_X | 0 | 0.037 |
| 0 | 0.037 | |
| YouTube | 0 | 0.068 |
# Games–Howell post-hoc per age
gh_results_age <- data_PCA3 %>%
tidyr::pivot_longer(
cols = dplyr::all_of(social_vars),
names_to = "Platform",
values_to = "Frequency"
) %>%
dplyr::group_by(Platform) %>%
rstatix::games_howell_test(Frequency ~ Age_cat) %>%
dplyr::mutate(
p_sig = dplyr::case_when(
p.adj < .001 ~ "***",
p.adj < .01 ~ "**",
p.adj < .05 ~ "*",
TRUE ~ ""
)
) %>%
dplyr::select(
Platform, group1, group2,
mean_difference = estimate, p.adj, p_sig
)
knitr::kable(
gh_results_age,
digits = 3,
caption = "Comparison post-hoc Games–Howell (for age class)"
)| Platform | group1 | group2 | mean_difference | p.adj | p_sig |
|---|---|---|---|---|---|
| 18_24 | 25_34 | 0.614 | 0.151 | ||
| 18_24 | 35_44 | 1.259 | 0.000 | *** | |
| 18_24 | 45_54 | 1.013 | 0.000 | *** | |
| 18_24 | 55_64 | 0.900 | 0.004 | ** | |
| 18_24 | 65_+ | 0.465 | 0.367 | ||
| 25_34 | 35_44 | 0.645 | 0.009 | ** | |
| 25_34 | 45_54 | 0.400 | 0.256 | ||
| 25_34 | 55_64 | 0.286 | 0.665 | ||
| 25_34 | 65_+ | -0.149 | 0.965 | ||
| 35_44 | 45_54 | -0.246 | 0.685 | ||
| 35_44 | 55_64 | -0.359 | 0.310 | ||
| 35_44 | 65_+ | -0.794 | 0.000 | *** | |
| 45_54 | 55_64 | -0.114 | 0.986 | ||
| 45_54 | 65_+ | -0.549 | 0.010 | * | |
| 55_64 | 65_+ | -0.435 | 0.106 | ||
| 18_24 | 25_34 | -0.351 | 0.507 | ||
| 18_24 | 35_44 | -0.558 | 0.069 | ||
| 18_24 | 45_54 | -1.386 | 0.000 | *** | |
| 18_24 | 55_64 | -1.836 | 0.000 | *** | |
| 18_24 | 65_+ | -2.866 | 0.000 | *** | |
| 25_34 | 35_44 | -0.208 | 0.876 | ||
| 25_34 | 45_54 | -1.035 | 0.000 | *** | |
| 25_34 | 55_64 | -1.485 | 0.000 | *** | |
| 25_34 | 65_+ | -2.515 | 0.000 | *** | |
| 35_44 | 45_54 | -0.827 | 0.000 | *** | |
| 35_44 | 55_64 | -1.277 | 0.000 | *** | |
| 35_44 | 65_+ | -2.308 | 0.000 | *** | |
| 45_54 | 55_64 | -0.450 | 0.181 | ||
| 45_54 | 65_+ | -1.480 | 0.000 | *** | |
| 55_64 | 65_+ | -1.031 | 0.000 | *** | |
| 18_24 | 25_34 | 0.440 | 0.480 | ||
| 18_24 | 35_44 | 0.494 | 0.340 | ||
| 18_24 | 45_54 | -0.220 | 0.922 | ||
| 18_24 | 55_64 | -0.468 | 0.291 | ||
| 18_24 | 65_+ | -0.858 | 0.001 | ** | |
| 25_34 | 35_44 | 0.054 | 1.000 | ||
| 25_34 | 45_54 | -0.660 | 0.004 | ** | |
| 25_34 | 55_64 | -0.908 | 0.000 | *** | |
| 25_34 | 65_+ | -1.298 | 0.000 | *** | |
| 35_44 | 45_54 | -0.714 | 0.001 | ** | |
| 35_44 | 55_64 | -0.962 | 0.000 | *** | |
| 35_44 | 65_+ | -1.352 | 0.000 | *** | |
| 45_54 | 55_64 | -0.249 | 0.519 | ||
| 45_54 | 65_+ | -0.639 | 0.000 | *** | |
| 55_64 | 65_+ | -0.390 | 0.027 | * | |
| Telegram | 18_24 | 25_34 | -0.123 | 0.997 | |
| Telegram | 18_24 | 35_44 | -0.160 | 0.990 | |
| Telegram | 18_24 | 45_54 | -0.707 | 0.053 | |
| Telegram | 18_24 | 55_64 | -1.080 | 0.000 | *** |
| Telegram | 18_24 | 65_+ | -1.875 | 0.000 | *** |
| Telegram | 25_34 | 35_44 | -0.037 | 1.000 | |
| Telegram | 25_34 | 45_54 | -0.584 | 0.064 | |
| Telegram | 25_34 | 55_64 | -0.957 | 0.000 | *** |
| Telegram | 25_34 | 65_+ | -1.752 | 0.000 | *** |
| Telegram | 35_44 | 45_54 | -0.548 | 0.093 | |
| Telegram | 35_44 | 55_64 | -0.921 | 0.000 | *** |
| Telegram | 35_44 | 65_+ | -1.716 | 0.000 | *** |
| Telegram | 45_54 | 55_64 | -0.373 | 0.349 | |
| Telegram | 45_54 | 65_+ | -1.168 | 0.000 | *** |
| Telegram | 55_64 | 65_+ | -0.795 | 0.000 | *** |
| TikTok | 18_24 | 25_34 | -1.110 | 0.003 | ** |
| TikTok | 18_24 | 35_44 | -1.573 | 0.000 | *** |
| TikTok | 18_24 | 45_54 | -1.718 | 0.000 | *** |
| TikTok | 18_24 | 55_64 | -2.125 | 0.000 | *** |
| TikTok | 18_24 | 65_+ | -2.324 | 0.000 | *** |
| TikTok | 25_34 | 35_44 | -0.463 | 0.373 | |
| TikTok | 25_34 | 45_54 | -0.608 | 0.074 | |
| TikTok | 25_34 | 55_64 | -1.015 | 0.000 | *** |
| TikTok | 25_34 | 65_+ | -1.215 | 0.000 | *** |
| TikTok | 35_44 | 45_54 | -0.145 | 0.981 | |
| TikTok | 35_44 | 55_64 | -0.552 | 0.057 | |
| TikTok | 35_44 | 65_+ | -0.751 | 0.001 | *** |
| TikTok | 45_54 | 55_64 | -0.407 | 0.204 | |
| TikTok | 45_54 | 65_+ | -0.607 | 0.003 | ** |
| TikTok | 55_64 | 65_+ | -0.200 | 0.773 | |
| Twitter_X | 18_24 | 25_34 | -0.342 | 0.819 | |
| Twitter_X | 18_24 | 35_44 | -0.474 | 0.482 | |
| Twitter_X | 18_24 | 45_54 | -0.909 | 0.004 | ** |
| Twitter_X | 18_24 | 55_64 | -1.015 | 0.001 | *** |
| Twitter_X | 18_24 | 65_+ | -1.169 | 0.000 | *** |
| Twitter_X | 25_34 | 35_44 | -0.133 | 0.993 | |
| Twitter_X | 25_34 | 45_54 | -0.568 | 0.075 | |
| Twitter_X | 25_34 | 55_64 | -0.674 | 0.018 | * |
| Twitter_X | 25_34 | 65_+ | -0.828 | 0.001 | *** |
| Twitter_X | 35_44 | 45_54 | -0.435 | 0.231 | |
| Twitter_X | 35_44 | 55_64 | -0.541 | 0.066 | |
| Twitter_X | 35_44 | 65_+ | -0.695 | 0.003 | ** |
| Twitter_X | 45_54 | 55_64 | -0.106 | 0.988 | |
| Twitter_X | 45_54 | 65_+ | -0.260 | 0.529 | |
| Twitter_X | 55_64 | 65_+ | -0.154 | 0.915 | |
| 18_24 | 25_34 | -0.159 | 0.825 | ||
| 18_24 | 35_44 | -0.021 | 1.000 | ||
| 18_24 | 45_54 | -0.058 | 0.996 | ||
| 18_24 | 55_64 | -0.205 | 0.536 | ||
| 18_24 | 65_+ | -0.617 | 0.000 | *** | |
| 25_34 | 35_44 | 0.138 | 0.863 | ||
| 25_34 | 45_54 | 0.101 | 0.932 | ||
| 25_34 | 55_64 | -0.046 | 0.999 | ||
| 25_34 | 65_+ | -0.458 | 0.002 | ** | |
| 35_44 | 45_54 | -0.037 | 0.999 | ||
| 35_44 | 55_64 | -0.184 | 0.559 | ||
| 35_44 | 65_+ | -0.596 | 0.000 | *** | |
| 45_54 | 55_64 | -0.147 | 0.630 | ||
| 45_54 | 65_+ | -0.559 | 0.000 | *** | |
| 55_64 | 65_+ | -0.413 | 0.003 | ** | |
| YouTube | 18_24 | 25_34 | -0.470 | 0.160 | |
| YouTube | 18_24 | 35_44 | -0.393 | 0.344 | |
| YouTube | 18_24 | 45_54 | -0.757 | 0.001 | *** |
| YouTube | 18_24 | 55_64 | -1.018 | 0.000 | *** |
| YouTube | 18_24 | 65_+ | -1.398 | 0.000 | *** |
| YouTube | 25_34 | 35_44 | 0.077 | 0.998 | |
| YouTube | 25_34 | 45_54 | -0.287 | 0.449 | |
| YouTube | 25_34 | 55_64 | -0.548 | 0.010 | * |
| YouTube | 25_34 | 65_+ | -0.928 | 0.000 | *** |
| YouTube | 35_44 | 45_54 | -0.364 | 0.194 | |
| YouTube | 35_44 | 55_64 | -0.625 | 0.002 | ** |
| YouTube | 35_44 | 65_+ | -1.005 | 0.000 | *** |
| YouTube | 45_54 | 55_64 | -0.260 | 0.470 | |
| YouTube | 45_54 | 65_+ | -0.641 | 0.000 | *** |
| YouTube | 55_64 | 65_+ | -0.380 | 0.074 |
# Graph for age
ggplot(desc_stats_age,
aes(x = Age_cat, y = mean, fill = Age_cat)) +
geom_col(position = "dodge", color = "grey30") +
geom_errorbar(aes(ymin = mean - se, ymax = mean + se),
width = 0.2,
position = position_dodge(0.9)) +
facet_wrap(~ Platform, scales = "free_y") +
labs(
title = "Platform use frequence and age class",
x = "Age classes",
y = "Platform use frequence (±SE)"
) +
scale_fill_viridis_d() +
theme_minimal() +
theme(
legend.position = "none",
axis.text.x = element_text(angle = 90, hjust = 1)
)
OLS regression model (Platform use as DV)
# 14. OLS
library(broom)Warning: il pacchetto 'broom' è stato creato con R versione 4.4.2library(car)Warning: il pacchetto 'car' è stato creato con R versione 4.4.3Caricamento del pacchetto richiesto: carDataWarning: il pacchetto 'carData' è stato creato con R versione 4.4.3
Caricamento pacchetto: 'car'Il seguente oggetto è mascherato da 'package:psych':
logitIl seguente oggetto è mascherato da 'package:dplyr':
recodelibrary(purrr)Warning: il pacchetto 'purrr' è stato creato con R versione 4.4.2
Caricamento pacchetto: 'purrr'Il seguente oggetto è mascherato da 'package:car':
someIl seguente oggetto è mascherato da 'package:scales':
discardlibrary(stringr)
# Dependent variables (platform use)
social_vars <- c("YouTube","WhatsApp","Twitter_X","Instagram",
"TikTok","Facebook","Linkedin","Telegram")
# Predictors (factors + latent factors of participation)
predictors <- c("Age_cat", "Vote", "Sex", "Left_Right",
"Pol_int", "Info_Online", "Partisan_mobil")
# OLS per each platform
ols_results <- purrr::map_df(social_vars, function(var) {
fml <- as.formula(
paste(var, "~", paste(predictors, collapse = " + "))
)
model <- lm(fml, data = data_PCA3)
coef_tbl <- broom::tidy(model, conf.int = TRUE) %>%
dplyr::mutate(
Dependent = var,
Adj_R2 = summary(model)$adj.r.squared,
F_stat = broom::glance(model)$statistic,
p_model = broom::glance(model)$p.value
)
# VIF and robust merge
vif_vals <- tryCatch(car::vif(model), error = function(e) NULL)
if (!is.null(vif_vals)) {
if (is.matrix(vif_vals)) {
vif_df <- tibble(
predictor_key = rownames(vif_vals),
VIF = vif_vals[, 1]
)
} else {
vif_df <- tibble(
predictor_key = names(vif_vals),
VIF = as.numeric(vif_vals)
)
}
predictor_pattern <- paste(predictors, collapse = "|")
coef_tbl <- coef_tbl %>%
dplyr::mutate(
predictor_key = stringr::str_extract(term, predictor_pattern)
) %>%
dplyr::left_join(vif_df, by = "predictor_key") %>%
dplyr::select(-predictor_key)
} else {
coef_tbl <- coef_tbl %>% dplyr::mutate(VIF = NA_real_)
}
coef_tbl
})
# Model fit
fit_summary <- ols_results %>%
dplyr::group_by(Dependent) %>%
dplyr::summarise(
Adj_R2 = unique(Adj_R2),
F_stat = unique(F_stat),
p_model = unique(p_model),
.groups = "drop"
)
knitr::kable(
fit_summary,
digits = 3,
caption = "Statistiche di fit del modello OLS per ciascuna piattaforma"
)| Dependent | Adj_R2 | F_stat | p_model |
|---|---|---|---|
| 0.111 | 5.697 | 0 | |
| 0.276 | 15.217 | 0 | |
| 0.168 | 8.360 | 0 | |
| Telegram | 0.177 | 8.815 | 0 |
| TikTok | 0.158 | 7.797 | 0 |
| Twitter_X | 0.141 | 6.953 | 0 |
| 0.063 | 3.540 | 0 | |
| YouTube | 0.135 | 6.866 | 0 |
# Coefficients table
coef_table <- ols_results %>%
dplyr::filter(term != "(Intercept)") %>%
dplyr::mutate(
Significant = ifelse(p.value < 0.05, "Yes", "No")
) %>%
dplyr::select(
Dependent, term, estimate, std.error,
conf.low, conf.high, p.value, VIF, Significant
)
knitr::kable(
coef_table,
digits = 3,
caption = "OLS coefficients (95% CI, VIF, significance)"
)| Dependent | term | estimate | std.error | conf.low | conf.high | p.value | VIF | Significant |
|---|---|---|---|---|---|---|---|---|
| YouTube | Age_cat25_34 | -0.357 | 0.249 | -0.847 | 0.132 | 0.152 | 1.215 | No |
| YouTube | Age_cat35_44 | -0.080 | 0.239 | -0.549 | 0.390 | 0.739 | 1.215 | No |
| YouTube | Age_cat45_54 | -0.788 | 0.229 | -1.238 | -0.338 | 0.001 | 1.215 | Yes |
| YouTube | Age_cat55_64 | -0.890 | 0.227 | -1.336 | -0.444 | 0.000 | 1.215 | Yes |
| YouTube | Age_cat65_+ | -1.354 | 0.216 | -1.779 | -0.929 | 0.000 | 1.215 | Yes |
| YouTube | VoteAlleanza Verdi-Sinistra | 0.711 | 0.349 | 0.027 | 1.394 | 0.042 | 3.310 | Yes |
| YouTube | VoteAzione-Italia Viva | 0.738 | 0.331 | 0.088 | 1.388 | 0.026 | 3.310 | Yes |
| YouTube | VoteDid not vote | 0.814 | 0.312 | 0.201 | 1.427 | 0.009 | 3.310 | Yes |
| YouTube | VoteForza Italia | 1.266 | 0.392 | 0.497 | 2.035 | 0.001 | 3.310 | Yes |
| YouTube | VoteFratelli d’Italia | 0.862 | 0.335 | 0.205 | 1.518 | 0.010 | 3.310 | Yes |
| YouTube | VoteLega | 1.015 | 0.379 | 0.271 | 1.759 | 0.008 | 3.310 | Yes |
| YouTube | VoteMovimento 5 Stelle | 0.825 | 0.305 | 0.226 | 1.424 | 0.007 | 3.310 | Yes |
| YouTube | VoteOther party | 0.840 | 0.340 | 0.172 | 1.507 | 0.014 | 3.310 | Yes |
| YouTube | VotePartito Democratico | 0.461 | 0.292 | -0.111 | 1.033 | 0.114 | 3.310 | No |
| YouTube | SexFemale | -0.124 | 0.105 | -0.330 | 0.083 | 0.240 | 1.148 | No |
| YouTube | SexNon-binary | 0.732 | 0.925 | -1.084 | 2.548 | 0.429 | 1.148 | No |
| YouTube | Left_RightLeft | 0.095 | 0.179 | -0.256 | 0.446 | 0.595 | 3.052 | No |
| YouTube | Left_RightCenter | 0.280 | 0.182 | -0.078 | 0.637 | 0.125 | 3.052 | No |
| YouTube | Left_RightRight | -0.033 | 0.230 | -0.484 | 0.418 | 0.886 | 3.052 | No |
| YouTube | Left_RightExt_right | 0.369 | 0.258 | -0.137 | 0.874 | 0.153 | 3.052 | No |
| YouTube | Left_RightNo_position | 0.390 | 0.212 | -0.027 | 0.806 | 0.066 | 3.052 | No |
| YouTube | Pol_intSomewhat interested | 0.419 | 0.137 | 0.151 | 0.688 | 0.002 | 1.661 | Yes |
| YouTube | Pol_intNot very interested | 0.338 | 0.173 | -0.002 | 0.678 | 0.051 | 1.661 | No |
| YouTube | Pol_intNot at all interested | 0.172 | 0.259 | -0.338 | 0.681 | 0.508 | 1.661 | No |
| YouTube | Info_Online | 0.100 | 0.071 | -0.040 | 0.240 | 0.161 | 1.679 | No |
| YouTube | Partisan_mobil | 0.302 | 0.076 | 0.154 | 0.451 | 0.000 | 1.917 | Yes |
| Age_cat25_34 | -0.056 | 0.195 | -0.440 | 0.327 | 0.774 | 1.219 | No | |
| Age_cat35_44 | -0.136 | 0.187 | -0.504 | 0.231 | 0.467 | 1.219 | No | |
| Age_cat45_54 | -0.124 | 0.180 | -0.477 | 0.229 | 0.492 | 1.219 | No | |
| Age_cat55_64 | -0.280 | 0.179 | -0.631 | 0.070 | 0.117 | 1.219 | No | |
| Age_cat65_+ | -0.692 | 0.170 | -1.026 | -0.359 | 0.000 | 1.219 | Yes | |
| VoteAlleanza Verdi-Sinistra | 0.038 | 0.274 | -0.499 | 0.576 | 0.888 | 3.319 | No | |
| VoteAzione-Italia Viva | -0.119 | 0.260 | -0.629 | 0.391 | 0.648 | 3.319 | No | |
| VoteDid not vote | -0.040 | 0.245 | -0.521 | 0.441 | 0.869 | 3.319 | No | |
| VoteForza Italia | 0.100 | 0.308 | -0.504 | 0.704 | 0.744 | 3.319 | No | |
| VoteFratelli d’Italia | 0.092 | 0.263 | -0.424 | 0.607 | 0.727 | 3.319 | No | |
| VoteLega | 0.162 | 0.296 | -0.420 | 0.743 | 0.585 | 3.319 | No | |
| VoteMovimento 5 Stelle | 0.179 | 0.240 | -0.292 | 0.650 | 0.456 | 3.319 | No | |
| VoteOther party | 0.049 | 0.266 | -0.474 | 0.571 | 0.856 | 3.319 | No | |
| VotePartito Democratico | 0.088 | 0.229 | -0.361 | 0.538 | 0.700 | 3.319 | No | |
| SexFemale | 0.220 | 0.082 | 0.059 | 0.382 | 0.008 | 1.146 | Yes | |
| SexNon-binary | 0.611 | 0.727 | -0.815 | 2.038 | 0.401 | 1.146 | No | |
| Left_RightLeft | 0.098 | 0.140 | -0.177 | 0.374 | 0.484 | 3.050 | No | |
| Left_RightCenter | 0.099 | 0.143 | -0.182 | 0.379 | 0.490 | 3.050 | No | |
| Left_RightRight | 0.034 | 0.180 | -0.319 | 0.388 | 0.849 | 3.050 | No | |
| Left_RightExt_right | 0.172 | 0.201 | -0.223 | 0.568 | 0.392 | 3.050 | No | |
| Left_RightNo_position | -0.245 | 0.167 | -0.571 | 0.082 | 0.142 | 3.050 | No | |
| Pol_intSomewhat interested | 0.163 | 0.107 | -0.047 | 0.372 | 0.128 | 1.642 | No | |
| Pol_intNot very interested | 0.252 | 0.135 | -0.014 | 0.517 | 0.063 | 1.642 | No | |
| Pol_intNot at all interested | 0.114 | 0.204 | -0.287 | 0.514 | 0.578 | 1.642 | No | |
| Info_Online | -0.069 | 0.055 | -0.178 | 0.040 | 0.212 | 1.670 | No | |
| Partisan_mobil | 0.239 | 0.059 | 0.123 | 0.355 | 0.000 | 1.914 | Yes | |
| Twitter_X | Age_cat25_34 | -0.239 | 0.284 | -0.796 | 0.317 | 0.399 | 1.225 | No |
| Twitter_X | Age_cat35_44 | -0.284 | 0.272 | -0.817 | 0.249 | 0.296 | 1.225 | No |
| Twitter_X | Age_cat45_54 | -0.701 | 0.262 | -1.215 | -0.188 | 0.007 | 1.225 | Yes |
| Twitter_X | Age_cat55_64 | -0.746 | 0.260 | -1.255 | -0.236 | 0.004 | 1.225 | Yes |
| Twitter_X | Age_cat65_+ | -0.843 | 0.247 | -1.328 | -0.358 | 0.001 | 1.225 | Yes |
| Twitter_X | VoteAlleanza Verdi-Sinistra | 0.207 | 0.401 | -0.580 | 0.995 | 0.606 | 3.260 | No |
| Twitter_X | VoteAzione-Italia Viva | 0.209 | 0.377 | -0.532 | 0.949 | 0.580 | 3.260 | No |
| Twitter_X | VoteDid not vote | 0.334 | 0.359 | -0.370 | 1.038 | 0.352 | 3.260 | No |
| Twitter_X | VoteForza Italia | 0.141 | 0.457 | -0.755 | 1.037 | 0.757 | 3.260 | No |
| Twitter_X | VoteFratelli d’Italia | -0.165 | 0.384 | -0.919 | 0.589 | 0.668 | 3.260 | No |
| Twitter_X | VoteLega | -0.030 | 0.437 | -0.888 | 0.828 | 0.945 | 3.260 | No |
| Twitter_X | VoteMovimento 5 Stelle | 0.058 | 0.350 | -0.630 | 0.745 | 0.869 | 3.260 | No |
| Twitter_X | VoteOther party | 0.482 | 0.388 | -0.281 | 1.244 | 0.215 | 3.260 | No |
| Twitter_X | VotePartito Democratico | 0.004 | 0.333 | -0.651 | 0.658 | 0.991 | 3.260 | No |
| Twitter_X | SexFemale | -0.082 | 0.121 | -0.319 | 0.155 | 0.495 | 1.146 | No |
| Twitter_X | SexNon-binary | 1.772 | 1.044 | -0.277 | 3.821 | 0.090 | 1.146 | No |
| Twitter_X | Left_RightLeft | 0.241 | 0.204 | -0.159 | 0.641 | 0.237 | 3.026 | No |
| Twitter_X | Left_RightCenter | 0.628 | 0.207 | 0.221 | 1.035 | 0.003 | 3.026 | Yes |
| Twitter_X | Left_RightRight | 0.646 | 0.263 | 0.130 | 1.162 | 0.014 | 3.026 | Yes |
| Twitter_X | Left_RightExt_right | 0.849 | 0.296 | 0.268 | 1.430 | 0.004 | 3.026 | Yes |
| Twitter_X | Left_RightNo_position | 0.368 | 0.246 | -0.115 | 0.851 | 0.135 | 3.026 | No |
| Twitter_X | Pol_intSomewhat interested | 0.095 | 0.156 | -0.210 | 0.401 | 0.541 | 1.648 | No |
| Twitter_X | Pol_intNot very interested | -0.174 | 0.200 | -0.567 | 0.218 | 0.383 | 1.648 | No |
| Twitter_X | Pol_intNot at all interested | -0.442 | 0.300 | -1.032 | 0.148 | 0.142 | 1.648 | No |
| Twitter_X | Info_Online | 0.201 | 0.081 | 0.042 | 0.359 | 0.013 | 1.679 | Yes |
| Twitter_X | Partisan_mobil | 0.487 | 0.086 | 0.317 | 0.656 | 0.000 | 1.911 | Yes |
| Age_cat25_34 | -0.347 | 0.301 | -0.938 | 0.244 | 0.250 | 1.221 | No | |
| Age_cat35_44 | -0.514 | 0.289 | -1.082 | 0.054 | 0.076 | 1.221 | No | |
| Age_cat45_54 | -1.292 | 0.278 | -1.838 | -0.747 | 0.000 | 1.221 | Yes | |
| Age_cat55_64 | -1.780 | 0.276 | -2.322 | -1.239 | 0.000 | 1.221 | Yes | |
| Age_cat65_+ | -2.911 | 0.262 | -3.426 | -2.396 | 0.000 | 1.221 | Yes | |
| VoteAlleanza Verdi-Sinistra | 0.768 | 0.424 | -0.064 | 1.599 | 0.070 | 3.294 | No | |
| VoteAzione-Italia Viva | 0.767 | 0.400 | -0.019 | 1.553 | 0.056 | 3.294 | No | |
| VoteDid not vote | 0.560 | 0.378 | -0.183 | 1.302 | 0.139 | 3.294 | No | |
| VoteForza Italia | 1.058 | 0.477 | 0.122 | 1.994 | 0.027 | 3.294 | Yes | |
| VoteFratelli d’Italia | 0.361 | 0.405 | -0.434 | 1.157 | 0.373 | 3.294 | No | |
| VoteLega | 0.692 | 0.460 | -0.211 | 1.595 | 0.133 | 3.294 | No | |
| VoteMovimento 5 Stelle | 0.638 | 0.370 | -0.088 | 1.364 | 0.085 | 3.294 | No | |
| VoteOther party | 0.112 | 0.414 | -0.700 | 0.924 | 0.787 | 3.294 | No | |
| VotePartito Democratico | 0.485 | 0.353 | -0.208 | 1.178 | 0.170 | 3.294 | No | |
| SexFemale | 0.884 | 0.128 | 0.633 | 1.134 | 0.000 | 1.145 | Yes | |
| SexNon-binary | -0.693 | 1.121 | -2.892 | 1.506 | 0.536 | 1.145 | No | |
| Left_RightLeft | 0.097 | 0.218 | -0.330 | 0.525 | 0.655 | 3.024 | No | |
| Left_RightCenter | 0.385 | 0.222 | -0.051 | 0.821 | 0.084 | 3.024 | No | |
| Left_RightRight | 0.338 | 0.280 | -0.212 | 0.887 | 0.228 | 3.024 | No | |
| Left_RightExt_right | 0.561 | 0.313 | -0.054 | 1.176 | 0.074 | 3.024 | No | |
| Left_RightNo_position | 0.070 | 0.261 | -0.442 | 0.582 | 0.788 | 3.024 | No | |
| Pol_intSomewhat interested | 0.238 | 0.165 | -0.086 | 0.563 | 0.150 | 1.651 | No | |
| Pol_intNot very interested | 0.275 | 0.210 | -0.137 | 0.686 | 0.191 | 1.651 | No | |
| Pol_intNot at all interested | 0.111 | 0.316 | -0.508 | 0.731 | 0.724 | 1.651 | No | |
| Info_Online | 0.163 | 0.086 | -0.005 | 0.331 | 0.057 | 1.670 | No | |
| Partisan_mobil | 0.326 | 0.092 | 0.146 | 0.507 | 0.000 | 1.919 | Yes | |
| TikTok | Age_cat25_34 | -1.137 | 0.296 | -1.718 | -0.556 | 0.000 | 1.230 | Yes |
| TikTok | Age_cat35_44 | -1.499 | 0.284 | -2.057 | -0.941 | 0.000 | 1.230 | Yes |
| TikTok | Age_cat45_54 | -1.601 | 0.274 | -2.138 | -1.063 | 0.000 | 1.230 | Yes |
| TikTok | Age_cat55_64 | -2.000 | 0.272 | -2.533 | -1.467 | 0.000 | 1.230 | Yes |
| TikTok | Age_cat65_+ | -2.136 | 0.258 | -2.643 | -1.629 | 0.000 | 1.230 | Yes |
| TikTok | VoteAlleanza Verdi-Sinistra | -0.505 | 0.418 | -1.326 | 0.316 | 0.227 | 3.303 | No |
| TikTok | VoteAzione-Italia Viva | -0.693 | 0.395 | -1.468 | 0.083 | 0.080 | 3.303 | No |
| TikTok | VoteDid not vote | -0.327 | 0.376 | -1.065 | 0.410 | 0.384 | 3.303 | No |
| TikTok | VoteForza Italia | -0.056 | 0.472 | -0.982 | 0.870 | 0.906 | 3.303 | No |
| TikTok | VoteFratelli d’Italia | -0.761 | 0.401 | -1.549 | 0.026 | 0.058 | 3.303 | No |
| TikTok | VoteLega | -0.481 | 0.453 | -1.370 | 0.408 | 0.289 | 3.303 | No |
| TikTok | VoteMovimento 5 Stelle | -0.034 | 0.367 | -0.754 | 0.687 | 0.927 | 3.303 | No |
| TikTok | VoteOther party | 0.044 | 0.411 | -0.763 | 0.851 | 0.915 | 3.303 | No |
| TikTok | VotePartito Democratico | -0.494 | 0.349 | -1.179 | 0.191 | 0.157 | 3.303 | No |
| TikTok | SexFemale | 0.374 | 0.126 | 0.126 | 0.622 | 0.003 | 1.150 | Yes |
| TikTok | SexNon-binary | 0.248 | 1.093 | -1.897 | 2.394 | 0.820 | 1.150 | No |
| TikTok | Left_RightLeft | -0.078 | 0.214 | -0.498 | 0.342 | 0.714 | 3.033 | No |
| TikTok | Left_RightCenter | 0.347 | 0.217 | -0.079 | 0.773 | 0.110 | 3.033 | No |
| TikTok | Left_RightRight | 0.694 | 0.274 | 0.157 | 1.231 | 0.011 | 3.033 | Yes |
| TikTok | Left_RightExt_right | 1.202 | 0.306 | 0.601 | 1.804 | 0.000 | 3.033 | Yes |
| TikTok | Left_RightNo_position | 0.392 | 0.256 | -0.111 | 0.894 | 0.127 | 3.033 | No |
| TikTok | Pol_intSomewhat interested | 0.488 | 0.163 | 0.168 | 0.808 | 0.003 | 1.646 | Yes |
| TikTok | Pol_intNot very interested | 0.437 | 0.208 | 0.029 | 0.845 | 0.036 | 1.646 | Yes |
| TikTok | Pol_intNot at all interested | -0.055 | 0.313 | -0.668 | 0.558 | 0.860 | 1.646 | No |
| TikTok | Info_Online | 0.356 | 0.084 | 0.190 | 0.521 | 0.000 | 1.672 | Yes |
| TikTok | Partisan_mobil | 0.096 | 0.090 | -0.082 | 0.273 | 0.290 | 1.915 | No |
| Age_cat25_34 | 0.631 | 0.299 | 0.046 | 1.217 | 0.035 | 1.215 | Yes | |
| Age_cat35_44 | 1.451 | 0.286 | 0.890 | 2.013 | 0.000 | 1.215 | Yes | |
| Age_cat45_54 | 1.163 | 0.274 | 0.625 | 1.702 | 0.000 | 1.215 | Yes | |
| Age_cat55_64 | 1.078 | 0.272 | 0.544 | 1.612 | 0.000 | 1.215 | Yes | |
| Age_cat65_+ | 0.571 | 0.259 | 0.062 | 1.080 | 0.028 | 1.215 | Yes | |
| VoteAlleanza Verdi-Sinistra | 0.716 | 0.416 | -0.100 | 1.532 | 0.085 | 3.331 | No | |
| VoteAzione-Italia Viva | 0.428 | 0.394 | -0.346 | 1.202 | 0.278 | 3.331 | No | |
| VoteDid not vote | 0.682 | 0.373 | -0.049 | 1.413 | 0.068 | 3.331 | No | |
| VoteForza Italia | 1.072 | 0.468 | 0.155 | 1.990 | 0.022 | 3.331 | Yes | |
| VoteFratelli d’Italia | 0.436 | 0.399 | -0.347 | 1.219 | 0.275 | 3.331 | No | |
| VoteLega | 0.994 | 0.451 | 0.108 | 1.880 | 0.028 | 3.331 | Yes | |
| VoteMovimento 5 Stelle | 1.136 | 0.365 | 0.419 | 1.852 | 0.002 | 3.331 | Yes | |
| VoteOther party | 0.239 | 0.405 | -0.557 | 1.034 | 0.556 | 3.331 | No | |
| VotePartito Democratico | 0.920 | 0.348 | 0.237 | 1.602 | 0.008 | 3.331 | Yes | |
| SexFemale | 0.627 | 0.125 | 0.381 | 0.873 | 0.000 | 1.147 | Yes | |
| SexNon-binary | 0.930 | 1.104 | -1.237 | 3.097 | 0.400 | 1.147 | No | |
| Left_RightLeft | -0.017 | 0.214 | -0.436 | 0.402 | 0.935 | 3.055 | No | |
| Left_RightCenter | 0.261 | 0.217 | -0.166 | 0.688 | 0.231 | 3.055 | No | |
| Left_RightRight | 0.499 | 0.274 | -0.039 | 1.037 | 0.069 | 3.055 | No | |
| Left_RightExt_right | 0.823 | 0.307 | 0.220 | 1.426 | 0.007 | 3.055 | Yes | |
| Left_RightNo_position | 0.055 | 0.254 | -0.443 | 0.553 | 0.828 | 3.055 | No | |
| Pol_intSomewhat interested | 0.100 | 0.162 | -0.219 | 0.418 | 0.538 | 1.652 | No | |
| Pol_intNot very interested | 0.426 | 0.206 | 0.022 | 0.831 | 0.039 | 1.652 | Yes | |
| Pol_intNot at all interested | 0.242 | 0.309 | -0.365 | 0.849 | 0.434 | 1.652 | No | |
| Info_Online | -0.001 | 0.085 | -0.168 | 0.167 | 0.993 | 1.669 | No | |
| Partisan_mobil | 0.499 | 0.090 | 0.322 | 0.676 | 0.000 | 1.908 | Yes | |
| Age_cat25_34 | 0.581 | 0.237 | 0.116 | 1.046 | 0.014 | 1.227 | Yes | |
| Age_cat35_44 | 0.820 | 0.227 | 0.374 | 1.266 | 0.000 | 1.227 | Yes | |
| Age_cat45_54 | 0.077 | 0.218 | -0.351 | 0.505 | 0.724 | 1.227 | No | |
| Age_cat55_64 | -0.031 | 0.217 | -0.457 | 0.395 | 0.886 | 1.227 | No | |
| Age_cat65_+ | -0.456 | 0.206 | -0.861 | -0.051 | 0.027 | 1.227 | Yes | |
| VoteAlleanza Verdi-Sinistra | 0.108 | 0.337 | -0.553 | 0.769 | 0.750 | 3.383 | No | |
| VoteAzione-Italia Viva | -0.103 | 0.318 | -0.726 | 0.521 | 0.747 | 3.383 | No | |
| VoteDid not vote | -0.193 | 0.301 | -0.783 | 0.398 | 0.522 | 3.383 | No | |
| VoteForza Italia | -0.309 | 0.378 | -1.050 | 0.432 | 0.413 | 3.383 | No | |
| VoteFratelli d’Italia | -0.826 | 0.324 | -1.462 | -0.189 | 0.011 | 3.383 | Yes | |
| VoteLega | -0.600 | 0.369 | -1.324 | 0.124 | 0.104 | 3.383 | No | |
| VoteMovimento 5 Stelle | -0.262 | 0.295 | -0.840 | 0.317 | 0.375 | 3.383 | No | |
| VoteOther party | -0.094 | 0.329 | -0.738 | 0.551 | 0.776 | 3.383 | No | |
| VotePartito Democratico | -0.014 | 0.281 | -0.565 | 0.537 | 0.960 | 3.383 | No | |
| SexFemale | -0.084 | 0.101 | -0.283 | 0.115 | 0.407 | 1.145 | No | |
| SexNon-binary | 0.000 | 0.878 | -1.723 | 1.723 | 1.000 | 1.145 | No | |
| Left_RightLeft | 0.103 | 0.171 | -0.234 | 0.439 | 0.549 | 3.083 | No | |
| Left_RightCenter | 0.712 | 0.174 | 0.370 | 1.054 | 0.000 | 3.083 | Yes | |
| Left_RightRight | 0.890 | 0.221 | 0.456 | 1.324 | 0.000 | 3.083 | Yes | |
| Left_RightExt_right | 1.004 | 0.248 | 0.518 | 1.491 | 0.000 | 3.083 | Yes | |
| Left_RightNo_position | 0.552 | 0.206 | 0.147 | 0.957 | 0.008 | 3.083 | Yes | |
| Pol_intSomewhat interested | 0.143 | 0.131 | -0.115 | 0.400 | 0.277 | 1.645 | No | |
| Pol_intNot very interested | -0.062 | 0.167 | -0.391 | 0.266 | 0.710 | 1.645 | No | |
| Pol_intNot at all interested | -0.406 | 0.252 | -0.901 | 0.089 | 0.108 | 1.645 | No | |
| Info_Online | 0.427 | 0.068 | 0.294 | 0.561 | 0.000 | 1.683 | Yes | |
| Partisan_mobil | 0.024 | 0.072 | -0.118 | 0.166 | 0.742 | 1.920 | No | |
| Telegram | Age_cat25_34 | -0.070 | 0.295 | -0.648 | 0.509 | 0.814 | 1.239 | No |
| Telegram | Age_cat35_44 | 0.033 | 0.283 | -0.522 | 0.588 | 0.906 | 1.239 | No |
| Telegram | Age_cat45_54 | -0.555 | 0.273 | -1.090 | -0.020 | 0.042 | 1.239 | Yes |
| Telegram | Age_cat55_64 | -0.789 | 0.270 | -1.319 | -0.260 | 0.004 | 1.239 | Yes |
| Telegram | Age_cat65_+ | -1.557 | 0.258 | -2.064 | -1.050 | 0.000 | 1.239 | Yes |
| Telegram | VoteAlleanza Verdi-Sinistra | 0.186 | 0.416 | -0.631 | 1.002 | 0.655 | 3.365 | No |
| Telegram | VoteAzione-Italia Viva | -0.261 | 0.394 | -1.035 | 0.512 | 0.507 | 3.365 | No |
| Telegram | VoteDid not vote | 0.277 | 0.374 | -0.457 | 1.011 | 0.459 | 3.365 | No |
| Telegram | VoteForza Italia | 0.237 | 0.472 | -0.690 | 1.164 | 0.616 | 3.365 | No |
| Telegram | VoteFratelli d’Italia | 0.158 | 0.401 | -0.630 | 0.946 | 0.695 | 3.365 | No |
| Telegram | VoteLega | 0.186 | 0.456 | -0.709 | 1.080 | 0.684 | 3.365 | No |
| Telegram | VoteMovimento 5 Stelle | 0.104 | 0.365 | -0.612 | 0.820 | 0.775 | 3.365 | No |
| Telegram | VoteOther party | 0.632 | 0.406 | -0.165 | 1.429 | 0.120 | 3.365 | No |
| Telegram | VotePartito Democratico | 0.112 | 0.348 | -0.570 | 0.794 | 0.747 | 3.365 | No |
| Telegram | SexFemale | -0.253 | 0.126 | -0.501 | -0.006 | 0.045 | 1.150 | Yes |
| Telegram | SexNon-binary | -0.240 | 1.088 | -2.375 | 1.896 | 0.826 | 1.150 | No |
| Telegram | Left_RightLeft | 0.643 | 0.213 | 0.225 | 1.061 | 0.003 | 3.067 | Yes |
| Telegram | Left_RightCenter | 0.832 | 0.217 | 0.407 | 1.258 | 0.000 | 3.067 | Yes |
| Telegram | Left_RightRight | 1.034 | 0.275 | 0.493 | 1.574 | 0.000 | 3.067 | Yes |
| Telegram | Left_RightExt_right | 1.276 | 0.309 | 0.669 | 1.883 | 0.000 | 3.067 | Yes |
| Telegram | Left_RightNo_position | 0.863 | 0.254 | 0.365 | 1.361 | 0.001 | 3.067 | Yes |
| Telegram | Pol_intSomewhat interested | 0.220 | 0.163 | -0.100 | 0.539 | 0.177 | 1.658 | No |
| Telegram | Pol_intNot very interested | 0.147 | 0.207 | -0.260 | 0.554 | 0.478 | 1.658 | No |
| Telegram | Pol_intNot at all interested | -0.106 | 0.312 | -0.717 | 0.506 | 0.734 | 1.658 | No |
| Telegram | Info_Online | 0.410 | 0.084 | 0.245 | 0.575 | 0.000 | 1.686 | Yes |
| Telegram | Partisan_mobil | 0.201 | 0.090 | 0.024 | 0.378 | 0.026 | 1.937 | Yes |
# Coefficient plot
coef_table %>%
dplyr::mutate(
term = stringr::str_replace_all(
term,
c(
"Age_cat" = "Age: ",
"Vote" = "Vote: ",
"Sex" = "Sex: ",
"Left_Right" = "L–R: ",
"Pol_int" = "Pol. interest: ",
"\\[T\\." = "",
"\\]" = ""
)
)
) %>%
ggplot(aes(
y = reorder(term, estimate),
x = estimate,
color = Significant,
shape = Significant
)) +
geom_point(position = position_dodge(width = 0.5), size = 1) +
geom_errorbar(aes(xmin = conf.low, xmax = conf.high),
height = 0.2,
position = position_dodge(width = 0.5),
orientation = "y") +
geom_vline(xintercept = 0, linetype = "dashed", color = "gray50") +
facet_wrap(~ Dependent, scales = "free_y", ncol = 2) +
scale_color_manual(values = c("No" = "gray50", "Yes" = "#E69F00")) +
scale_shape_manual(values = c("No" = 16, "Yes" = 17)) +
theme_bw(base_size = 6) +
theme(
legend.position = "bottom",
panel.grid.minor = element_blank(),
panel.grid.major.y = element_blank(),
strip.background = element_rect(fill = "gray90"),
axis.text.y = element_text(size = 4)
) +
labs(
title = "OLS coefficients per platform",
subtitle = "Statistically significant coefficients (p < .05)",
y = "Predictors",
x = "Coefficient extimation (±95% CI)",
color = "Significant:",
shape = "Significant:"
)`height` was translated to `width`.