Swift Analysis
Dr. Ken Blake
2024-10-14
Brandwatch data
Data collection began by instructing Brandwatch to search social media content posted from Feb. 1 through June 1, 2024, using the following query:
"Taylor Swift" AND ("Tortured Poets" OR "#TSTTPD" OR "#TTPD" OR "#TTPDTheAnthology") NOT "RT @"
The query, which automatically switched to a 44.92% sampling rate due to the sheer volume of the matching content, yielded about 41.7 million posts from accounts identified by Brandwatch as “individual” accounts and another approximately 8.6 million posts from accounts identified by Brandwatch as “organizational” accounts.
Plotted by day, in units of 1,000 posts, both individual and organizational volume showed a distinct peak during the latter half of April, a period corresponding to the “Tortured Poets” album release.
# Required packages
if (!require("tidyverse"))
install.packages("tidyverse")
if (!require("gmodels"))
install.packages("gmodels")
if (!require("gtsummary"))
install.packages("gtsummary")
library(tidyverse)
library(gmodels)
library(ggplot2)
library(gtsummary)
library(readxl)
# Get Brandwatch daily volume summary data
# https://github.com/drkblake/Data/raw/main/Brandwatch%20TTPD%20mention%20volume%20in%20thousands%20Feb%20to%20Jun.xlsx
download.file(
"https://github.com/drkblake/Data/raw/main/Brandwatch%20TTPD%20mention%20volume%20in%20thousands%20Feb%20to%20Jun.xlsx",
"FebToJune.xlsx",
quiet = TRUE,
mode = "wb"
)
FebToJune <- read_xlsx("FebToJune.xlsx", sheet = "Long")
FebToJune$days <- as.POSIXct(FebToJune$days, tz = "America/Chicago")
sum(FebToJune$individual)*1000## [1] 41686000sum(FebToJune$organisational)*1000## [1] 8625000# Reformatting
FebToJune2 <- FebToJune %>%
rename(Date = days,
Individual = individual,
Organizational = organisational) %>%
select(Date, Individual, Organizational) %>%
gather(key = "Source", value = "Posts", -Date)
# Visualization
ggplot(FebToJune2, aes(x = Date, y = Posts)) +
geom_line(aes(color = Source, linetype = Source)) +
scale_color_manual(values = c("darkred", "steelblue"))
Considering the obvious April peak and the limitations of the full period’s sampling rate of well under half of the relevant posts, the analysis focused on posts shared between April 14 and May 1, 2024. The album released on April 19, 2024.
Repeating the above Brandwatch query for posts shared between April 14 and May 1, 2024, yielded 168,828 posts. Because of the smaller volume, Brandwatch retrieved all available posts instead of only a sample. The standard Brandwatch export includes 135 variables, many relevant to only one of the monitored platforms. The analysis retained only seven.
# Read from local file
SwiftData <- read.csv("SwiftData.csv")
# Getting all column names
ColumnNames <- as.data.frame(colnames(SwiftData))
# Keeping columns of interest after making a copy
# of the full dataset for reference
FullSwiftData <- SwiftData
SwiftData <- select(SwiftData,
Date,
Url,
Domain,
Page.Type,
Account.Type,
Author,
Full.Text)
# Removing any duplicate rows
SwiftData <- SwiftData %>%
distinct(Url, .keep_all = TRUE)
#Formatting "Date" as POSIXct object
SwiftData$Date <- as.POSIXct(SwiftData$Date, tz = "America/Chicago")
#Sorting by Date
SwiftData <- arrange(SwiftData,Date)
glimpse(SwiftData)## Rows: 168,828
## Columns: 7
## $ Date <dttm> 2024-04-14 05:00:00, 2024-04-14 05:00:00, 2024-04-14 05:…
## $ Url <chr> "https://hiindia.com/taylor-swift-travis-kelce-kiss-and-d…
## $ Domain <chr> "hiindia.com", "arabherald.com", "calgarymonitor.com", "p…
## $ Page.Type <chr> "news", "news", "news", "news", "news", "news", "news", "…
## $ Account.Type <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "…
## $ Author <chr> "Indi A", "", "Voice of America14 Apr 2024, 21:35 GMT+10"…
## $ Full.Text <chr> "Los Angeles, April 14 (IANS) Singer-songwriter Taylor Sw…Categorizing posts by type of source
The Brandwatch query retrieved posts from nine different platforms, including “news” platforms, a category consisting of web sites for online news operations. Analysis found 60,749 posts from such “news” platforms. A recode added to the “news” category 5,680 X/Twitter posts from sources marked “organisational” by X/Twitter. All other posts, including all other posts from X/Twitter, were classified as posts from “individual” sources.
#Categorizing by source type
PageTable <- SwiftData %>%
select(Page.Type) %>%
tbl_summary()
PageTable| Characteristic | N = 168,8281 |
|---|---|
| Page.Type | |
| blog | 795 (0.5%) |
| facebook_public | 6,499 (3.8%) |
| forum | 1,474 (0.9%) |
| news | 60,749 (36%) |
| 33,973 (20%) | |
| review | 13 (<0.1%) |
| tumblr | 28,385 (17%) |
| 36,450 (22%) | |
| youtube | 490 (0.3%) |
| 1 n (%) | |
SwiftData <- SwiftData %>%
mutate(SourceType =
case_when(
Page.Type == "news" ~ "News",
Page.Type == "twitter" &
Account.Type == "organisational" ~ "News",
TRUE ~ "Individual"))
SourceTable <- SwiftData %>%
select(SourceType) %>%
tbl_summary()
SourceTable| Characteristic | N = 168,8281 |
|---|---|
| SourceType | |
| Individual | 102,399 (61%) |
| News | 66,429 (39%) |
| 1 n (%) | |
Chi-squared test of source type proportions
Obviously, the proportion of individual posts greatly exceeded the proportion of news posts. But was the difference statistically significant? A Chi-squared test can give an answer:
SourceType_test <- chisq.test(x = table(SwiftData$SourceType))
SourceType_test##
## Chi-squared test for given probabilities
##
## data: table(SwiftData$SourceType)
## X-squared = 7663.7, df = 1, p-value < 2.2e-16Yes. The proportion of individual posts significantly exceeded the proportion of news posts, X2(1, N = 168,828) = 7,663.7, p < .0001.
Daily counts, by source type
With a few exceptions, daily counts of individual posts outstripped daily counts of news posts:
# Re-expressing "Date" as "Day," the day from "Date."
SwiftData <- SwiftData %>%
mutate(Day = floor_date(Date,
unit = "day"))
# Graphing posts per day
PostsByDay <- SwiftData %>%
group_by(Day, SourceType) %>%
summarize(
Posts = n()) %>%
rename(Source = SourceType)
ggplot(PostsByDay, aes(x = Day, y = Posts)) +
geom_line(aes(color = Source, linetype = Source)) +
scale_color_manual(values = c("darkred", "steelblue"))
rm(PostsByDay)Hunting Easter eggs
The analysis attempted to identify Easter-egg-themed posts by searching for key words and phrases associated with each of nine frequently-alluded-to Easter eggs. Here are the search terms for each, followed by counts and percentages for the number of posts treated as associated with each egg.
# Easter egg variables
# Check for "thanK you aIMee" egg
SwiftData <- SwiftData %>%
mutate(
aIMee = case_when(
grepl("thanK you aIMee", Full.Text) ~ "aIMee egg",
grepl("Kardashian", Full.Text) ~ "aIMee egg",
grepl("diss track", Full.Text) ~ "aIMee egg",
grepl("bully", Full.Text) ~ "aIMee egg",
grepl("lessons learned", Full.Text) ~ "aIMee egg",
grepl(" Kim ", Full.Text) ~ "aIMee egg",
TRUE ~ "Other topic"
)
)
# Check for "So High School" egg
SwiftData <- SwiftData %>%
mutate(
HighSchool = case_when(
grepl("So High School", Full.Text) ~ "High School egg",
grepl("Kelce", Full.Text) ~ "High School egg",
grepl("Marry Kiss or Kill me", Full.Text) ~ "High School egg",
grepl("how to ball", Full.Text) ~ "High School egg",
grepl("I know Aristotle", Full.Text) ~ "High School egg",
grepl("love track", Full.Text) ~ "High School egg",
TRUE ~ "Other topic"
)
)
# Check for "So Long, London" egg
SwiftData <- SwiftData %>%
mutate(
London = case_when(
grepl("So Long, London", Full.Text) ~ "London egg",
grepl("diss track", Full.Text) ~ "London egg",
grepl("heartbreak song", Full.Text) ~ "London egg",
grepl("Joe Alwyn", Full.Text) ~ "London egg",
grepl("London Boy", Full.Text) ~ "London egg",
grepl("Had a good run", Full.Text) ~ "London egg",
grepl("demise of relationship", Full.Text) ~ "London egg",
grepl("Every day of a love affair", Full.Text) ~ "London egg",
TRUE ~ "Other topic"
)
)
# Check for "The Black Dog" egg
SwiftData <- SwiftData %>%
mutate(
BlackDog = case_when(
grepl("The Black Dog", Full.Text) ~ "Black Dog egg",
grepl(" pub ", Full.Text) ~ "Black Dog egg",
grepl(" ex ", Full.Text) ~ "Black Dog egg",
grepl(" bar ", Full.Text) ~ "Black Dog egg",
grepl("forgetting his location", Full.Text) ~ "Black Dog egg",
TRUE ~ "Other topic"
)
)
# Check for "The Alchemy" egg
SwiftData <- SwiftData %>%
mutate(
Alchemy = case_when(
grepl("The Alchemy", Full.Text) ~ "Alchemy egg",
grepl("Kelce", Full.Text) ~ "Alchemy egg",
grepl("Travis", Full.Text) ~ "Alchemy egg",
grepl(" Chiefs ", Full.Text) ~ "Alchemy egg",
grepl("love song", Full.Text) ~ "Alchemy egg",
grepl("Where's the trophy", Full.Text) ~ "Alchemy egg",
grepl("he comes running over to me", Full.Text) ~ "Alchemy egg",
grepl(
"So when I touch down, call the amateurs and cut em from the team",
Full.Text
) ~ "Alchemy egg",
TRUE ~ "Other topic"
)
)
# Check for "Cassandra" egg
SwiftData <- SwiftData %>%
mutate(
Cassandra = case_when(
grepl("Cassandra", Full.Text) ~ "Cassandra egg",
grepl("KimYe", Full.Text) ~ "Cassandra egg",
grepl("Greek Mythology", Full.Text) ~ "Cassandra egg",
grepl("snake imagery", Full.Text) ~ "Cassandra egg",
grepl("doomed to see the future", Full.Text) ~ "Cassandra egg",
grepl("no one believes her", Full.Text) ~ "Cassandra egg",
grepl(" greed ", Full.Text) ~ "Cassandra egg",
grepl("family", Full.Text) ~ "Cassandra egg",
grepl("Kardashian-West diss track", Full.Text) ~ "Cassandra egg",
TRUE ~ "Other topic"
)
)
# Check for "I Hate it Here" egg
SwiftData <- SwiftData %>%
mutate(
HateitHere = case_when(
grepl("I Hate it Here", Full.Text) ~ "Hate it Here egg",
grepl("Alwyn", Full.Text) ~ "Hate it Here egg",
grepl(" ex ", Full.Text) ~ "Hate it Here egg",
grepl("relationship was painful", Full.Text) ~ "Hate it Here egg",
grepl("hiding in her relationship", Full.Text) ~ "Hate it Here egg",
TRUE ~ "Other topic"
)
)
# Check for "2am Release of second album" egg
SwiftData <- SwiftData %>%
mutate(
SecondAlbum = case_when(
grepl("2am Release of second album", Full.Text) ~ "Second Album Time Release egg",
grepl("Clock in Midnights room was 2am", Full.Text) ~ "Second Album Time Release egg",
grepl("2 fingers during album annoucement", Full.Text) ~ "Second Album Time Release egg",
grepl("past mentions of 2am in past songs", Full.Text) ~ "Second Album Time Release egg",
grepl("pocket watch in Bejewled video", Full.Text) ~ "Second Album Time Release egg",
grepl("double album", Full.Text) ~ "Second Album Time Release egg",
grepl("31 songs", Full.Text) ~ "Second Album Time Release egg",
TRUE ~ "Other topic"
)
)
# Check for "Album Release Date" egg
SwiftData <- SwiftData %>%
mutate(
ReleaseDate = case_when(
grepl("Album Release Date", Full.Text) ~ "Release Date egg",
grepl("famous dinner", Full.Text) ~ "Release Date egg",
grepl("Blake Lively", Full.Text) ~ "Release Date egg",
grepl("Ryan Reynolds", Full.Text) ~ "Release Date egg",
grepl("announced breakup", Full.Text) ~ "Release Date egg",
grepl("April 19th", Full.Text) ~ "Release Date egg",
grepl("all unfollowed Alwyn", Full.Text) ~ "Release Date egg",
TRUE ~ "Other topic"
)
)
# Check for generic "Easter egg" mention
SwiftData <- SwiftData %>%
mutate(
GenericEgg = case_when(
grepl("Easter egg", Full.Text) ~ "Generic egg",
grepl("Easter eggs", Full.Text) ~ "Generic egg",
TRUE ~ "Other topic"
)
)
# How many of each egg?
EggTable <- SwiftData %>%
select(
aIMee,
HighSchool,
London,
BlackDog,
Alchemy,
Cassandra,
HateitHere,
SecondAlbum,
ReleaseDate,
GenericEgg,
) %>%
tbl_summary()
EggTable| Characteristic | N = 168,8281 |
|---|---|
| aIMee | |
| aIMee egg | 4,221 (2.5%) |
| Other topic | 164,607 (97%) |
| HighSchool | |
| High School egg | 5,919 (3.5%) |
| Other topic | 162,909 (96%) |
| London | |
| London egg | 5,617 (3.3%) |
| Other topic | 163,211 (97%) |
| BlackDog | |
| Black Dog egg | 3,017 (1.8%) |
| Other topic | 165,811 (98%) |
| Alchemy | |
| Alchemy egg | 6,887 (4.1%) |
| Other topic | 161,941 (96%) |
| Cassandra | |
| Cassandra egg | 1,548 (0.9%) |
| Other topic | 167,280 (99%) |
| HateitHere | |
| Hate it Here egg | 4,149 (2.5%) |
| Other topic | 164,679 (98%) |
| SecondAlbum | |
| Other topic | 162,461 (96%) |
| Second Album Time Release egg | 6,367 (3.8%) |
| ReleaseDate | |
| Other topic | 168,287 (100%) |
| Release Date egg | 541 (0.3%) |
| GenericEgg | |
| Generic egg | 778 (0.5%) |
| Other topic | 168,050 (100%) |
| 1 n (%) | |
Easter egg counts by source type
The analysis explored whether the number of posts qualifying as Easter egg posts varied significantly by source type. First, some code to categorize each post as alluding to at least one of the specified eggs or as alluding to none of the specified eggs:
# Quant transformation of Easter egg variables
SwiftData <- SwiftData %>%
mutate(aIMee = case_when(
grepl("thanK you aIMee", Full.Text) ~ 1,
grepl("Kardashian", Full.Text) ~ 1,
grepl("diss track", Full.Text) ~ 1,
grepl("bully", Full.Text) ~ 1,
grepl("lessons learned", Full.Text) ~ 1,
grepl(" Kim ", Full.Text) ~ 1,
TRUE ~ 0
))
# Check for "So High School" egg
SwiftData <- SwiftData %>%
mutate(HighSchool = case_when(
grepl("So High School", Full.Text) ~ 1,
grepl("Kelce", Full.Text) ~ 1,
grepl("Marry Kiss or Kill me", Full.Text) ~ 1,
grepl("how to ball", Full.Text) ~ 1,
grepl("I know Aristotle", Full.Text) ~ 1,
grepl("love track", Full.Text) ~ 1,
TRUE ~ 0
))
# Check for "So Long, London" egg
SwiftData <- SwiftData %>%
mutate(
London = case_when(
grepl("So Long, London", Full.Text) ~ 1,
grepl("diss track", Full.Text) ~ 1,
grepl("heartbreak song", Full.Text) ~ 1,
grepl("Joe Alwyn", Full.Text) ~ 1,
grepl("London Boy", Full.Text) ~ 1,
grepl("Had a good run", Full.Text) ~ 1,
grepl("demise of relationship", Full.Text) ~ 1,
grepl("Every day of a love affair", Full.Text) ~ 1,
TRUE ~ 0
)
)
# Check for "The Black Dog" egg
SwiftData <- SwiftData %>%
mutate(BlackDog = case_when(
grepl("The Black Dog", Full.Text) ~ 1,
grepl(" pub ", Full.Text) ~ 1,
grepl(" ex ", Full.Text) ~ 1,
grepl(" bar ", Full.Text) ~ 1,
grepl("forgetting his location", Full.Text) ~ 1,
TRUE ~ 0
))
# Check for "The Alchemy" egg
SwiftData <- SwiftData %>%
mutate(
Alchemy = case_when(
grepl("The Alchemy", Full.Text) ~ 1,
grepl("Kelce", Full.Text) ~ 1,
grepl("Travis", Full.Text) ~ 1,
grepl(" Chiefs ", Full.Text) ~ 1,
grepl("love song", Full.Text) ~ 1,
grepl("Where's the trophy", Full.Text) ~ 1,
grepl("he comes running over to me", Full.Text) ~ 1,
grepl(
"So when I touch down, call the amateurs and cut em from the team",
Full.Text
) ~ 1,
TRUE ~ 0
)
)
# Check for "Cassandra" egg
SwiftData <- SwiftData %>%
mutate(
Cassandra = case_when(
grepl("Cassandra", Full.Text) ~ 1,
grepl("KimYe", Full.Text) ~ 1,
grepl("Greek Mythology", Full.Text) ~ 1,
grepl("snake imagery", Full.Text) ~ 1,
grepl("doomed to see the future", Full.Text) ~ 1,
grepl("no one believes her", Full.Text) ~ 1,
grepl(" greed ", Full.Text) ~ 1,
grepl("family", Full.Text) ~ 1,
grepl("Kardashian-West diss track", Full.Text) ~ 1,
TRUE ~ 0
)
)
# Check for "I Hate it Here" egg
SwiftData <- SwiftData %>%
mutate(HateitHere = case_when(
grepl("I Hate it Here", Full.Text) ~ 1,
grepl("Alwyn", Full.Text) ~ 1,
grepl(" ex ", Full.Text) ~ 1,
grepl("relationship was painful", Full.Text) ~ 1,
grepl("hiding in her relationship", Full.Text) ~ 1,
TRUE ~ 0
))
# Check for "2am Release of second album" egg
SwiftData <- SwiftData %>%
mutate(
SecondAlbum = case_when(
grepl("2am Release of second album", Full.Text) ~ 1,
grepl("Clock in Midnights room was 2am", Full.Text) ~ 1,
grepl("2 fingers during album annoucement", Full.Text) ~ 1,
grepl("past mentions of 2am in past songs", Full.Text) ~ 1,
grepl("pocket watch in Bejewled video", Full.Text) ~ 1,
grepl("double album", Full.Text) ~ 1,
grepl("31 songs", Full.Text) ~ 1,
TRUE ~ 0))
# Check for "Album Release Date" egg
SwiftData <- SwiftData %>%
mutate(
ReleaseDate = case_when(
grepl("Album Release Date", Full.Text) ~ 1,
grepl("famous dinner", Full.Text) ~ 1,
grepl("Blake Lively", Full.Text) ~ 1,
grepl("Ryan Reynolds", Full.Text) ~ 1,
grepl("annouced breakup", Full.Text) ~ 1,
grepl("April 19th", Full.Text) ~ 1,
grepl("all unfollowed Alwyn", Full.Text) ~ 1,
TRUE ~ 0
)
)
# Check for generic "Easter egg" mention
SwiftData <- SwiftData %>%
mutate(
GenericEgg = case_when(
grepl("Easter egg", Full.Text) ~ 1,
grepl("Easter eggs", Full.Text) ~ 1,
TRUE ~ 0
)
)
# Exploring media vs. individuals
SwiftData <- SwiftData %>%
mutate(
EasterEggs = aIMee +
HighSchool +
London +
BlackDog +
Alchemy +
Cassandra +
HateitHere +
SecondAlbum +
ReleaseDate +
GenericEgg
) %>%
mutate(AnyEgg = case_when(EasterEggs > 0 ~ "Egg", TRUE ~ "No egg"))Next, a crosstabulation table, which shows that 24% of the posts from news sources alluded to an Easter egg, compared to only 8.2% of the posts from individuals:
Crosstab <- SwiftData %>%
tbl_cross(row = AnyEgg,
col = SourceType,
percent = "column")
Crosstab| SourceType | Total | ||
|---|---|---|---|
| Individual | News | ||
| AnyEgg | |||
| Egg | 8,662 (8.5%) | 16,163 (24%) | 24,825 (15%) |
| No egg | 93,737 (92%) | 50,266 (76%) | 144,003 (85%) |
| Total | 102,399 (100%) | 66,429 (100%) | 168,828 (100%) |
A chi-squared test found the pattern statistically significant:
# Specify the DV and IV
SwiftData$DV <- SwiftData$AnyEgg #Edit YOURDVNAME
SwiftData$IV <- SwiftData$SourceType #Edit YOURIVNAME
# Run the chi-squared test
options(scipen = 999)
chitestresults <- chisq.test(SwiftData$DV, SwiftData$IV)
chitestresults##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: SwiftData$DV and SwiftData$IV
## X-squared = 8091.7, df = 1, p-value < 0.00000000000000022Easter egg counts by type of fan
It’s challenging to identify, through quantitative content analysis, Taylor Swift fans who are likely to have developed parasocial relationship with Swift. The approach used here began by using Brandwatch to download all replies to the three posts Swift shared on Twitter during the late-April 2024 time period being examined. Swift’s three posts were:
https://x.com/taylorswift13/status/1779486593587315020, in which Swift promoted an exclusive vinyl version of the album from Target.
https://x.com/taylorswift13/status/1782803343858860248, in which Swift shared a link to a Billboard.com review of the album.
https://x.com/taylorswift13/status/1783220925480997314, in which Swift shared a link of an NPR.org review of the album.
The approach assumed that individuals in a parasocial relationship with Swift would have replied to at least one of these three posts by Swift. It did not assume, however, that all replies to the three posts came from such fans. Even a cursory look through the posts reveals many that are critical of Swift, aimed at promoting unrelated ideas, etc.
This code retrieves a file of Brandwatch data containing the text of each reply, the reply author’s user name, and more. It also writes a copy of the file to the user’s hard drive.
# Get data on replies to three Swift posts
Fans <- read.csv("https://github.com/drkblake/Data/raw/main/Fans.csv")
write.csv(Fans,"Fans.csv", row.names = FALSE)The “NPR Review” post drew the most replies, followed by the “Billboard review” post, and then the “Target promo” post:
# Reply counts by post
ReplyTable <- Fans %>%
select(Post) %>%
tbl_summary()
ReplyTable| Characteristic | N = 3,0451 |
|---|---|
| Post | |
| Billboard review | 1,333 (44%) |
| NPR review | 1,479 (49%) |
| Target promo | 233 (7.7%) |
| 1 n (%) | |
The approach assumed that Twitter users who had replied to at least one of Swift’s posts and also had shared a post captured by the Brandwatch search for posts about the album would be at least somewhat likely to have formed a parasocial relationship with Swift.
In keeping with this assumption, a list of each user who had posted at least one reply to at least one of Swift’s posts was extracted, then merged with the full dataset, so that each post in the full dataset could be identified has having come from a potential “PSRFan,” who had replied to at least one of Swift’s posts, or some “Other” type of user.
This code accomplishes the merge, then produces a crosstabulation showing the number of Easter egg posts among “PSR Fans” and “Others.” It also saves a copy of the merged file, SwiftData2.csv, to the user’s computer.
# Extract list of Twitter IDs from Fans data
FanList <- Fans %>%
group_by(Author) %>%
summarize(PSRPosts = n())
# Merge list of Fan IDs with larger dataset
# Flag each post as from a "PSRFan" or "Other"
SwiftData2 <- merge(SwiftData,FanList,
by="Author",
all.x = TRUE)
SwiftData2 <- SwiftData2 %>%
mutate(PSRPosts = coalesce(PSRPosts,0)) %>%
mutate(PSRFan = case_when(PSRPosts > 0 ~ "PSRFan",
TRUE ~ "Other"))
write.csv(SwiftData2, "SwiftData2.csv",
row.names = FALSE)
# Get count of PSR fans
if (!require("gtExtras"))
install.packages("gtExtras")## Loading required package: gtExtras## Loading required package: gtlibrary(gtExtras)
PSRFanPosters <- SwiftData2 %>%
filter(PSRFan == "PSRFan") %>%
group_by(Author) %>%
summarize(Posts = n()) %>%
arrange(desc(Posts))
nrow(PSRFanPosters)## [1] 559gt(PSRFanPosters) %>%
cols_align(align = "center") %>%
gt_theme_538 | Author | Posts |
|---|---|
| SwiftNYC | 37 |
| kenziesversion | 24 |
| happinessforts | 22 |
| imhharry | 21 |
| ZanaHajiSaid | 20 |
| ToniMarieKeys | 18 |
| reputanush13 | 18 |
| swiftarmy1989 | 18 |
| thisisabbytryin | 18 |
| dhart0412 | 17 |
| myhappinesstayy | 16 |
| wakinginthedark | 16 |
| RaghavsRep | 15 |
| getawaycargirlz | 15 |
| learn_with_san | 15 |
| alltooriah | 14 |
| emsswiftieadv | 13 |
| folksyswift | 13 |
| kaleighsversion | 13 |
| kloetheswiftie | 13 |
| INA_YH95 | 12 |
| corneliastagain | 12 |
| shookswiftie | 12 |
| stilllgotscars | 12 |
| PatriotPointman | 11 |
| fairyytaleswift | 11 |
| icouldstaylor | 11 |
| kubaqzn | 11 |
| Fityeth | 10 |
| alltoohailee | 10 |
| wkswift13 | 10 |
| ExileftNini | 9 |
| OmgItsMeAllison | 9 |
| soscarletsar | 9 |
| swiftie1ncanada | 9 |
| swiftsvrse | 9 |
| holygroundsound | 8 |
| tswizzle89girl | 8 |
| AllTooSammi13 | 7 |
| bigelam | 7 |
| bisScottTea | 7 |
| cowboylikediane | 7 |
| inafolklore | 7 |
| kush07_ | 7 |
| mirrorswifft | 7 |
| swifferwins | 7 |
| CorneliaDynasty | 6 |
| CourtneySong_ | 6 |
| aurorabora518 | 6 |
| becky_maroon | 6 |
| bejeweltay | 6 |
| ciwywisteria | 6 |
| ddenver13 | 6 |
| deathbytaylor4 | 6 |
| exiledarren | 6 |
| hiss_swift_hiss | 6 |
| just__meraki | 6 |
| karmaisamack | 6 |
| lucia_swiftiee | 6 |
| remisversion | 6 |
| EmilyG234 | 5 |
| KarmaIsAFad | 5 |
| MichaelMou61314 | 5 |
| SwiftCriminal13 | 5 |
| crumblingcait13 | 5 |
| difficultswift | 5 |
| doepikapadukone | 5 |
| fatihahere | 5 |
| folkIegends | 5 |
| iamhaunted13 | 5 |
| intheweeeds | 5 |
| lysstaysversion | 5 |
| metsswift | 5 |
| musikistlife | 5 |
| nothlngsnew | 5 |
| repftv | 5 |
| romaricharlz13 | 5 |
| solongldn | 5 |
| sweeetener | 5 |
| the13manuscript | 5 |
| theopinionline | 5 |
| tizthedamnszn_ | 5 |
| vallovestswift | 5 |
| zaralovestaylor | 5 |
| AlexStarlight13 | 4 |
| Dakotasversion | 4 |
| Mr_Swift1594 | 4 |
| SwiftieLR | 4 |
| __theantihero__ | 4 |
| annasflicker | 4 |
| jasonlipshutz | 4 |
| jessica_parks | 4 |
| karmaisagad | 4 |
| mirrorballaly | 4 |
| mirrorballydia | 4 |
| sIeeepyeyes | 4 |
| sabsevermore | 4 |
| safeandsoundhan | 4 |
| skysmirrorball | 4 |
| sobrittgoes | 4 |
| swiftiealyza | 4 |
| thisiscetrying | 4 |
| tis_sjoberg | 4 |
| 1mogenn | 3 |
| 4nd123w_ | 3 |
| CL_Hayate | 3 |
| NiamhlovesTay13 | 3 |
| Rebecca_CW13 | 3 |
| SwiftSocial1989 | 3 |
| TS19_89 | 3 |
| That_July_9 | 3 |
| TisTheDamnPhD | 3 |
| al3xaplaytswift | 3 |
| anarchistswift | 3 |
| benstafford100 | 3 |
| brarda_emilia | 3 |
| chloeg_13 | 3 |
| cjaemd | 3 |
| cmaikerujei | 3 |
| eggscrams | 3 |
| everslay13 | 3 |
| f0lkl0r313 | 3 |
| fearlesslygrace | 3 |
| folkhwore | 3 |
| gleam_twinkle | 3 |
| graynovember13 | 3 |
| imyoelvy | 3 |
| inezbythelakes | 3 |
| ivygrows1213 | 3 |
| japrilss | 3 |
| lexofmyheart | 3 |
| liekeisootw | 3 |
| littletookind | 3 |
| lovestephy | 3 |
| mirrorballkate | 3 |
| ninaswiftie13 | 3 |
| ninetiestrend | 3 |
| oncornelias | 3 |
| polkadotrryy | 3 |
| porcocteles | 3 |
| savyouaseat | 3 |
| serge_golubev | 3 |
| simplyyswiftie | 3 |
| sippingaugust | 3 |
| sweetnothingnew | 3 |
| teacherswiftie | 3 |
| thegreatwar13 | 3 |
| tooth4taylor | 3 |
| vinyuh | 3 |
| weallgotcrowns7 | 3 |
| 1lactiflora9 | 2 |
| 9tay8tay9_ | 2 |
| Abbylylee | 2 |
| Alyssa_TV13 | 2 |
| BTaylorVersion | 2 |
| Bestoptionisme | 2 |
| COLLAGECHARTS | 2 |
| Cikyugindayo | 2 |
| EurovisionSwift | 2 |
| GauchoTeddy | 2 |
| GetawayCaroline | 2 |
| HannahSapp505 | 2 |
| HollyIvyDaisy13 | 2 |
| JStandridge_ | 2 |
| JomirBrands | 2 |
| JordynLWYMMD | 2 |
| KeyFactoryNYC | 2 |
| MariaLoyoC | 2 |
| MikaSwift13 | 2 |
| MothestMo | 2 |
| OrdinaryfoolNJ | 2 |
| RepGirlTV1989 | 2 |
| Rickymidnights | 2 |
| RicoMorcielo | 2 |
| RockinBlondieTS | 2 |
| SelsGraceful | 2 |
| Swiftie092 | 2 |
| Swiftlyness1989 | 2 |
| Swifts__Version | 2 |
| Swiftsloverx | 2 |
| Tcheckx | 2 |
| UrbanVicki9324 | 2 |
| VaniaRobles24 | 2 |
| VinSwift13 | 2 |
| WhiTaysVersion | 2 |
| afroallura | 2 |
| alexsscardigan | 2 |
| ashleyegreer | 2 |
| autisticswiftie | 2 |
| autumnhoaxes | 2 |
| beasreputation | 2 |
| bejeweledselena | 2 |
| beyonceBGKC | 2 |
| bloom_bloom___ | 2 |
| bwswiftcats | 2 |
| castlscrumblng7 | 2 |
| champgns | 2 |
| chellabey | 2 |
| dani_readyforit | 2 |
| diancuh | 2 |
| elainereadz | 2 |
| ellebellelvr | 2 |
| everaishamore | 2 |
| exiledpoetsdept | 2 |
| faithless_hoax | 2 |
| flkloreee | 2 |
| floridaamii | 2 |
| footnqtes | 2 |
| gagasyuyi | 2 |
| grcelivia | 2 |
| happilylauren | 2 |
| hearthandsswift | 2 |
| henryjekylls | 2 |
| hotasswiftin | 2 |
| i_poetrylove | 2 |
| iammrswift | 2 |
| illicitayfairs | 2 |
| ionafyfe | 2 |
| ioveyourparty | 2 |
| isaactheswiftie | 2 |
| jjsversion | 2 |
| jordynsversion | 2 |
| kaiamal13 | 2 |
| kiwik94 | 2 |
| lauralynn1955 | 2 |
| lavenderhanz | 2 |
| lavenderrain31 | 2 |
| lindsheartstayx | 2 |
| lizzyyy1989 | 2 |
| londonbcy | 2 |
| longlivereptour | 2 |
| longlivethtlook | 2 |
| maamstfu | 2 |
| mattydedios | 2 |
| meawromantics | 2 |
| missbraziliana | 2 |
| ninfetinhacis | 2 |
| notcoolbanana | 2 |
| oIdcardlgan | 2 |
| obsesedlily | 2 |
| pinkpurp11 | 2 |
| polaroidofus_tv | 2 |
| rachutation | 2 |
| repuhtayytion | 2 |
| roses_are_tay13 | 2 |
| santaclaraswift | 2 |
| saydontgos | 2 |
| scrofanoswift | 2 |
| shinythingswift | 2 |
| sirentheswiftie | 2 |
| smollerpauly | 2 |
| sparklysari | 2 |
| spell1ngisfunn | 2 |
| stillruinmylife | 2 |
| swift_af_23 | 2 |
| swiftharry2 | 2 |
| swiftie_livie13 | 2 |
| swiftiedayas | 2 |
| swifttvs | 2 |
| taylorsivygrows | 2 |
| theeladybird13 | 2 |
| thisisluutrying | 2 |
| tinaxxwash | 2 |
| tswiftsttpd | 2 |
| waoyfswift | 2 |
| writsbyme | 2 |
| yailpersonified | 2 |
| 1989swiftafboi | 1 |
| AllinderLi81282 | 1 |
| B1azeYouStart3d | 1 |
| BernThisWey | 1 |
| CCConstantina | 1 |
| Carlos05443850 | 1 |
| CodyCaston13 | 1 |
| Crazyfangirl1Jo | 1 |
| D88673933D | 1 |
| DanaBrigoli | 1 |
| Dont_Blame_Bri | 1 |
| EVERG0D | 1 |
| EmilyyyHale | 1 |
| Enchantedivy2 | 1 |
| EpiphanyStan725 | 1 |
| EternalJolie_ | 1 |
| F3MININOMENON | 1 |
| FORNlGHT | 1 |
| GabrielleL11535 | 1 |
| GoodGuyGingy | 1 |
| HoIyJosee | 1 |
| IceWolf_17 | 1 |
| Ilariaisreading | 1 |
| ImChaos_Revelry | 1 |
| Iondonboylouis | 1 |
| Jen4tro | 1 |
| KarmaIsAQuinn | 1 |
| KevinThebar | 1 |
| LONGLlVE_ | 1 |
| LauraEden13 | 1 |
| LilyMeade | 1 |
| LongLiveBee13 | 1 |
| MBSanAndreas13 | 1 |
| Mads_IsTrying13 | 1 |
| MarkyMark_Jr81 | 1 |
| MidnightsMads_ | 1 |
| MindofRollins | 1 |
| MotherSwiftly | 1 |
| OrangesAndJess | 1 |
| PurrfectlyTS | 1 |
| RhodeToLove | 1 |
| Rithikisabitch | 1 |
| RrChaaaa | 1 |
| SWIFTSGETAWAAY | 1 |
| ScottishLovatic | 1 |
| Sertralineque3n | 1 |
| Sir6ALotB | 1 |
| Stanlyapp | 1 |
| StreamOver99 | 1 |
| Streamqueen486 | 1 |
| TTPDBolter | 1 |
| Taylorsswift_22 | 1 |
| ThatFanGirl1194 | 1 |
| TheGoatfox | 1 |
| VictorVfoc | 1 |
| WaiYanM76350781 | 1 |
| Whit5Eve | 1 |
| _Exile_Cardigan | 1 |
| _Jacob89_ | 1 |
| _ashleeeyc | 1 |
| _bariumsulfide | 1 |
| _finallycleann | 1 |
| ablikefolksongs | 1 |
| adisynev | 1 |
| aleftespos | 1 |
| alenasfolksongs | 1 |
| ali_too_well | 1 |
| alossstar | 1 |
| alyssitaffairs | 1 |
| amigorzito | 1 |
| aotydored | 1 |
| aswiftieaccount | 1 |
| augustxaudrey | 1 |
| averysavy | 1 |
| baIdshelby | 1 |
| bbejeweledd | 1 |
| bbyiknowplaces | 1 |
| beginagqin | 1 |
| bigrepstan | 1 |
| bloodmoonlit97 | 1 |
| braxtonmg | 1 |
| breebrandnew | 1 |
| btweentheliines | 1 |
| cantdolife2 | 1 |
| castlescrmblng | 1 |
| cattitude_girl | 1 |
| chiaswizzle | 1 |
| chinitoprablems | 1 |
| clarabowswift | 1 |
| cmtswift | 1 |
| comebackbehere | 1 |
| comet5inthe5ky | 1 |
| cosyswift | 1 |
| cowboyIikejuli | 1 |
| cowboy_like_ava | 1 |
| cowboylikegabs | 1 |
| cowboylikehale | 1 |
| cowboylikejac0b | 1 |
| cr3lintentions | 1 |
| crvstify | 1 |
| daenerfs | 1 |
| dancedintherain | 1 |
| dani_kindiskey | 1 |
| dayasbrina | 1 |
| daylight_poetry | 1 |
| daylighttwtz | 1 |
| dontwannashar3 | 1 |
| dreamtimeswift | 1 |
| dudaswift13_ | 1 |
| dwohtdeluxe | 1 |
| ejosefineee13 | 1 |
| electriqforit | 1 |
| enchantedstan89 | 1 |
| endlessfebruary | 1 |
| envythewoods | 1 |
| erastour0324 | 1 |
| erastourhoe | 1 |
| evegotbitten | 1 |
| everspeak13 | 1 |
| evremoree | 1 |
| f1replace_ash3s | 1 |
| fangirlburner | 1 |
| farmerswiftie | 1 |
| fearlvess | 1 |
| finelinelouiswt | 1 |
| fireflymystery | 1 |
| fireplaceeashes | 1 |
| folklaura_ | 1 |
| folklore_nfr | 1 |
| folkloreemind | 1 |
| folklorestarss | 1 |
| folkmoonlore | 1 |
| folktayles | 1 |
| foreverwiiinter | 1 |
| foreverwinterbb | 1 |
| forksmycity | 1 |
| fumerossantiago | 1 |
| gabssfm08 | 1 |
| gabyeskye13 | 1 |
| georgiastarss | 1 |
| getawaykate13 | 1 |
| gilmour34 | 1 |
| gra6cee | 1 |
| harrypurplebow | 1 |
| hauntedbymyrep | 1 |
| hauntedstaylor | 1 |
| hauntedua | 1 |
| hbwannie | 1 |
| heydorothea | 1 |
| hitsdiffrent13 | 1 |
| homesicklauren | 1 |
| hrts4dorothea | 1 |
| huspsa | 1 |
| ilovedylandchlo | 1 |
| imamirrorbll | 1 |
| imgonnagetubacc | 1 |
| inmydreamlanddd | 1 |
| inshadesofwrong | 1 |
| invisibleasu | 1 |
| itxsev | 1 |
| itzsdc | 1 |
| ivyromance | 1 |
| izzykitty27 | 1 |
| jacketisyours | 1 |
| janexxxmw | 1 |
| janineteaques | 1 |
| jeannielangz | 1 |
| jo0rgesandoval_ | 1 |
| jrglennon | 1 |
| juliewrestling | 1 |
| kaearnah | 1 |
| karmaismybtrfly | 1 |
| kendramunozz | 1 |
| krisofmyheart | 1 |
| lacyafterglow | 1 |
| larkofthemisery | 1 |
| lavenderxwonder | 1 |
| lavgays13 | 1 |
| leesiebridgers | 1 |
| lexis8685 | 1 |
| liberTAYrian | 1 |
| lilly30_ | 1 |
| llamarswift13 | 1 |
| lostinamericaa | 1 |
| loversloml | 1 |
| lyingtraitors | 1 |
| magick_bones | 1 |
| mariana_pereira | 1 |
| marisaswiftie | 1 |
| maroontv13 | 1 |
| meghanosleyy_ | 1 |
| meiaheartstay | 1 |
| midnightsashley | 1 |
| milasagna | 1 |
| moeralu | 1 |
| moonberryswift | 1 |
| moontosaturns | 1 |
| nansversion | 1 |
| naylortation13 | 1 |
| newmaserati89 | 1 |
| niallsshining | 1 |
| nintenceI | 1 |
| nobootaynocrime | 1 |
| noellepolo | 1 |
| nottayloras | 1 |
| nrendipity | 1 |
| oflittleoldme13 | 1 |
| ohmyreputaytion | 1 |
| onastarlitnight | 1 |
| ovrdrmaticntrue | 1 |
| painTStheEras | 1 |
| pikachooseyou | 1 |
| pinkspeaknow | 1 |
| plaasticheartss | 1 |
| plainrocktweets | 1 |
| plasticoffdsofa | 1 |
| popsisunsteady | 1 |
| pqristay | 1 |
| prentisslvr | 1 |
| raniascardigan | 1 |
| ranimisra02 | 1 |
| replixie | 1 |
| repswifttt13 | 1 |
| reputasentv | 1 |
| reputationdave_ | 1 |
| reqnights | 1 |
| reyhannswift13 | 1 |
| rightwylmm | 1 |
| rissarep_nights | 1 |
| samgirldean | 1 |
| sapphiretearsky | 1 |
| savinswift | 1 |
| scarlettcrowns | 1 |
| secretpluto13 | 1 |
| shelleysilver3 | 1 |
| silent_partner_ | 1 |
| skinnysel | 1 |
| softpippa | 1 |
| sohighschooll | 1 |
| solongdeareader | 1 |
| sophialoves1989 | 1 |
| speaknowdb | 1 |
| sportyswiftie89 | 1 |
| stateofdijon13 | 1 |
| stillswiffafboi | 1 |
| stormiewrites | 1 |
| stormsinmyeyez | 1 |
| sweetenermadboy | 1 |
| swiftclown | 1 |
| swiftgetawaytay | 1 |
| swiftly_gracie | 1 |
| swiftsantihero | 1 |
| swiftstrange | 1 |
| swiftwithonearm | 1 |
| tayIorsel | 1 |
| tayazula | 1 |
| taydailey8 | 1 |
| taydaloo | 1 |
| tayearns | 1 |
| taysversiononly | 1 |
| thatskye_ | 1 |
| theboywiths | 1 |
| thegreatwrs | 1 |
| thehauntedbell | 1 |
| thepoetsintern | 1 |
| thereinlieserin | 1 |
| thislovve | 1 |
| timeless_era_ | 1 |
| tio_victor_ | 1 |
| tisthedamnpoets | 1 |
| tlgadfeatrena | 1 |
| tori_fetzer | 1 |
| tortured_poettt | 1 |
| tsignelin | 1 |
| tswiftmeowqueen | 1 |
| tswizzle_ontop | 1 |
| ttpddolly | 1 |
| uncommon_cns | 1 |
| velucifer | 1 |
| wIwsfilm | 1 |
| watchedithappen | 1 |
| wednesdaycxfe | 1 |
| wendytay606 | 1 |
| whenwebothfell | 1 |
| wisteria1ane | 1 |
| wmtswift | 1 |
| worshipthislcve | 1 |
| yisingkao | 1 |
| yousaydontgo | 1 |
| yoyokxts | 1 |
| zbridgery | 1 |
| zionlovingswift | 1 |
# Crosstab and chi-square
Crosstab2 <- SwiftData2 %>%
tbl_cross(row = AnyEgg,
col = PSRFan,
percent = "column")
Crosstab2| PSRFan | Total | ||
|---|---|---|---|
| Other | PSRFan | ||
| AnyEgg | |||
| Egg | 24,772 (15%) | 53 (3.4%) | 24,825 (15%) |
| No egg | 142,514 (85%) | 1,489 (97%) | 144,003 (85%) |
| Total | 167,286 (100%) | 1,542 (100%) | 168,828 (100%) |
The approach identified 1,542 PSR fans - or at least potential ones - but only 53 of them (3.4%) shared a posts that met the criteria for posts that alluded to an Easter egg. Thus, the approach was not able to detect evidence of a link between engaging with Easter egg information and having a parasocial relationship with Swift. This approach focused exclusively on Twitter/X content. Such a link might be more evident on other platforms not covered by the approach. It also might be more evident in other analytical approaches. Future research could, and should, explore this connection further, through means other than those tried here.
Overall, though, the results of this analysis suggest Swift’s Easter eggs probably play a more evident role in agenda setting processes than in parasocial relationship formation processes. Easter eggs, and Swift fans’ fascination with them, perhaps create an easy way for media organizations to produce lucrative content about a Swift album release. A similar process might, in fact, explain Easter eggs’ popularity among Swift fans. Fans craving connection with other fans on social media, or social media rewards such as “likes” and followers, might see posting about Easter eggs as a way to attract the attention that leads to these benefits.
Qualitative analysis
Quantitative analysis of manifest content can miss nuances that qualitative analysis might reveal. Accordingly, 100-case random samples were extracted from key data frames in the analysis so that the full text of the sampled posts could be examined qualitatively.
Fans_sample <- sample_n(Fans,100)
Fan_egg_posts <- SwiftData2 %>%
filter(PSRFan == "PSRFan" & AnyEgg == "Egg")
Fan_no_egg_posts_sample <- SwiftData2 %>%
filter(PSRFan == "PSRFan" & AnyEgg == "No egg")
Fan_no_egg_posts_sample <- sample_n(Fan_no_egg_posts_sample,100)
write.csv(Fans_sample,
"Fans_sample.csv",
row.names = FALSE)
write.csv(Fan_egg_posts,
"Fan_egg_posts.csv",
row.names = FALSE)
write.csv(Fan_no_egg_posts_sample,
"Fan_no_egg_posts_sample.csv",
row.names = FALSE)