Rationale

Second-level agenda-setting theory expands upon first-level agenda-setting by examining not only which issues the media cover but also which aspects, or attributes, of those issues are emphasized. Because media outlets face limited space and resources, editorial choices must be made about which attributes to highlight. These choices create competition between attributes, with enforcement-oriented perspectives potentially dominating coverage while humanitarian or rights-oriented perspectives receive less attention.

Drawing on second-level agenda-setting theory, this project compares the salience of two attributes in U.S. immigration coverage: immigrant arrests (enforcement-focused coverage) and immigrant rights (protection-focused coverage). Specifically, the analysis examines weekly APNews.com stories published between January 1 and September 30, 2025, identifying which stories focus on enforcement versus rights-oriented topics. The results contribute to understanding how U.S. immigration coverage emphasizes different attributes, revealing which aspects of immigration receive greater visibility on the media agenda.

Hypothesis

Weekly APNews.com coverage of immigrant arrests and immigrant rights differed in salience during the first nine months of 2025, with enforcement-oriented coverage expected to appear more frequently.

Variables & Methods

The dependent variable was attribute salience, measured as the weekly count of articles mentioning key enforcement- or rights-oriented phrases.

The independent variable was story topic, categorized as “Immigrant Arrest” or “Immigrant Rights.”

Key words and phrases used to identify stories about immigrant arrests included: “immigrant enforcement,” “deportation,” “ICE arrest,” “border patrol,” and “detention.”

Key words and phrases used to identify stories about immigrant rights included: “asylum,” “DACA,” “refugee,” “migrant protection,” “family reunification,” and “humanitarian aid.”

Eventually, a paired-samples t-test will be used to assess whether differences in tone between the two topics are statistically significant.

Result & Discussions

The figure below displays the weekly distribution of APNews.com stories referencing immigrant arrests and immigrant rights between January 1 and September 30, 2025.

Descriptive Statistics: Pair Differences
count mean sd min max
39.000 2.897 4.216 −6.000 11.000
Normality Test (Shapiro-Wilk)
statistic p.value method
0.9724 0.4430 Shapiro-Wilk normality test
If the P.VALUE is 0.05 or less, the number of pairs is fewer than 40, and the distribution of pair differences shows obvious non-normality or outliers, consider using the Wilcoxon Signed Rank Test results instead of the Paired-Samples t-Test results.
Paired-Samples t-Test
statistic parameter p.value conf.low conf.high method
4.2914 38 0.0001 1.5306 4.2643 Paired t-test
Group Means and SDs (t-Test)
V1_Mean V2_Mean V1_SD V2_SD
4.718 7.615 3.203 3.711

Overall, coverage of immigrant arrests appeared more frequently and showed greater fluctuation throughout the period. Peaks in enforcement-related stories occurred around Weeks 19, 26, and 35, reflecting heightened reporting on border detentions, deportation operations, or policy announcements.

By contrast, immigrant rights stories were less frequent and generally stable, with modest increases during Weeks 4 and 8, periods associated with asylum rulings and community advocacy efforts.

A notable pattern is that spikes in enforcement coverage often coincided with declines in rights-oriented reporting, suggesting that these two attributes compete for visibility within APNews.com’s immigration agenda. When enforcement narratives gain prominence, humanitarian or protection-focused perspectives tend to recede.

These results support the expectations of second-level agenda-setting theory, indicating that the media emphasize certain attributes of immigration more strongly than others. The consistent dominance of enforcement-related coverage implies that audiences are more likely exposed to immigration as a law-and-order issue rather than as a humanitarian or social-justice concern. A forthcoming paired-samples t-test will further determine whether the weekly difference in coverage between these two attributes is statistically significant.

Code

# ============================================
# APNews text analysis (Second-level agenda-setting theory version)
# ============================================

# ============================================
# --- Load required libraries ---
# ============================================

if (!require("tidyverse")) install.packages("tidyverse")
if (!require("tidytext")) install.packages("tidytext")

library(tidyverse)
library(tidytext)

# ============================================
# --- Load the APNews data ---
# ============================================

# Read the data from the web
FetchedData <- readRDS(url("https://github.com/drkblake/Data/raw/refs/heads/main/APNews.rds"))
# Save the data on your computer
saveRDS(FetchedData, file = "APNews.rds")
# remove the downloaded data from the environment
rm (FetchedData)

APNews <- readRDS("APNews.rds")

# ============================================
# --- Define and apply FilterTopic ---
# ============================================

# --- Define FilterTopic phrases ---
FilterTopic_phrases <- c(
  "immigration", 
  "immigrant", 
  "DACA", 
  "asylum", 
  "deportation", 
  "ICE", 
  "border patrol", 
  "refugee"
)

# --- Escape regex special characters ---
escaped_FilterTopic <- str_replace_all(
  FilterTopic_phrases,
  "([\\^$.|?*+()\\[\\]{}\\\\])",
  "\\\\\\1"
)

# --- Build whole-word/phrase regex pattern ---
FilterTopic_pattern <- paste0("\\b", escaped_FilterTopic, "\\b", collapse = "|")

# --- Flag stories matching the FilterTopic ---
APNews <- APNews %>%
  mutate(
    Full.Text.clean = str_squish(Full.Text),
    FilterTopic = if_else(
      str_detect(Full.Text.clean, regex(FilterTopic_pattern, ignore_case = TRUE)),
      "Yes",
      "No"
    )
  )

# --- Create a TopicNews data frame consisting only of FilterTopic stories ---
TopicNews <- APNews %>%
  filter(FilterTopic == "Yes")

# ============================================
# --- Flag Topic1-related stories (within TopicNews) ---
# ============================================

# --- Define Topic1 phrases ---
phrases <- c(
  "immigration enforcement", 
  "deportation", 
  "ICE arrests", 
  "border patrol"
)

# --- Escape regex special characters ---
escaped_phrases <- str_replace_all(
  phrases,
  "([\\^$.|?*+()\\[\\]{}\\\\])",
  "\\\\\\1"
)

# --- Build pattern and apply matching ---
pattern <- paste0("\\b", escaped_phrases, "\\b", collapse = "|")

TopicNews <- TopicNews %>%
  mutate(
    Topic1 = if_else(
      str_detect(Full.Text.clean, regex(pattern, ignore_case = TRUE)),
      "Yes",
      "No"
    )
  )

# ============================================
# --- Flag Topic2-related stories (within TopicNews) ---
# ============================================

# --- Define Topic2 phrases ---
phrases <- c(
  "asylum", 
  "DACA", 
  "refugee", 
  "migrant protection"
)

# --- Escape regex special characters ---
escaped_phrases <- str_replace_all(
  phrases,
  "([\\^$.|?*+()\\[\\]{}\\\\])",
  "\\\\\\1"
)

# --- Build pattern and apply matching ---
pattern <- paste0("\\b", escaped_phrases, "\\b", collapse = "|")

TopicNews <- TopicNews %>%
  mutate(
    Topic2 = if_else(
      str_detect(Full.Text.clean, regex(pattern, ignore_case = TRUE)),
      "Yes",
      "No"
    )
  )

# ============================================
# --- Visualize weekly counts of Topic1- and Topic2-related stories ---
# ============================================

if (!require("plotly")) install.packages("plotly")
library(plotly)

# --- Summarize weekly counts for Topic1 = "Yes" ---
Topic1_weekly <- TopicNews %>%
  filter(Topic1 == "Yes") %>%
  group_by(Week) %>%
  summarize(Count = n(), .groups = "drop") %>%
  mutate(Topic = "Immigrant Arrest") # Note custom Topic1 label

# --- Summarize weekly counts for Topic2 = "Yes" ---
Topic2_weekly <- TopicNews %>%
  filter(Topic2 == "Yes") %>%
  group_by(Week) %>%
  summarize(Count = n(), .groups = "drop") %>%
  mutate(Topic = "Immigrant Rights") # Note custom Topic2 label

# --- Combine both summaries into one data frame ---
Weekly_counts <- bind_rows(Topic2_weekly, Topic1_weekly)

# --- Fill in missing combinations with zero counts ---
Weekly_counts <- Weekly_counts %>%
  tidyr::complete(
    Topic,
    Week = full_seq(range(Week), 1),  # generate all week numbers
    fill = list(Count = 0)
  ) %>%
  arrange(Topic, Week)

# --- Create interactive plotly line chart ---
AS2 <- plot_ly(
  data = Weekly_counts,
  x = ~Week,
  y = ~Count,
  color = ~Topic,
  colors = c("steelblue", "firebrick"),
  type = "scatter",
  mode = "lines+markers",
  line = list(width = 2),
  marker = list(size = 6)
) %>%
  layout(
    title = "Weekly Counts of Topic1- and Topic2-Related Stories within the FilterTopic Dataset",
    xaxis = list(
      title = "Week Number (starting with Week 1 of 2025)",
      dtick = 1
    ),
    yaxis = list(title = "Number of Articles"),
    legend = list(title = list(text = "Topic")),
    hovermode = "x unified"
  )

# ============================================
# --- Show the chart ---
# ============================================

AS2

# ============================================================
#  Setup: Install and Load Required Packages
# ============================================================
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("plotly")) install.packages("plotly")
if (!require("gt")) install.packages("gt")
if (!require("gtExtras")) install.packages("gtExtras")
if (!require("broom")) install.packages("broom")

library(tidyverse)
library(plotly)
library(gt)
library(gtExtras)
library(broom)

options(scipen = 999)

# ============================================================
#  Data Import
# ============================================================
# Reshape to wide form

mydata <- Weekly_counts %>%
  pivot_wider(names_from = Topic, values_from = Count)
names(mydata) <- make.names(names(mydata))

# Specify the two variables involved
mydata$V1 <- mydata$Immigrant.Rights # <== Customize this
mydata$V2 <- mydata$Immigrant.Arrest # <== Customize this

# ============================================================
#  Compute Pair Differences
# ============================================================
mydata$PairDifferences <- mydata$V2 - mydata$V1

# ============================================================
#  Interactive Histogram of Pair Differences
# ============================================================
hist_plot <- plot_ly(
  data = mydata,
  x = ~PairDifferences,
  type = "histogram",
  marker = list(color = "#1f78b4", line = list(color = "black", width = 1))
) %>%
  layout(
    title = "Distribution of Pair Differences",
    xaxis = list(title = "Pair Differences"),
    yaxis = list(title = "Count"),
    shapes = list(
      list(
        type = "line",
        x0 = mean(mydata$PairDifferences, na.rm = TRUE),
        x1 = mean(mydata$PairDifferences, na.rm = TRUE),
        y0 = 0,
        y1 = max(table(mydata$PairDifferences)),
        line = list(color = "red", dash = "dash")
      )
    )
  )

# ============================================================
#  Descriptive Statistics
# ============================================================
desc_stats <- mydata %>%
  summarise(
    count = n(),
    mean = mean(PairDifferences, na.rm = TRUE),
    sd = sd(PairDifferences, na.rm = TRUE),
    min = min(PairDifferences, na.rm = TRUE),
    max = max(PairDifferences, na.rm = TRUE)
  )

desc_table <- desc_stats %>%
  gt() %>%
  gt_theme_538() %>%
  tab_header(title = "Descriptive Statistics: Pair Differences") %>%
  fmt_number(columns = where(is.numeric), decimals = 3)

# ============================================================
#  Normality Test (Shapiro-Wilk)
# ============================================================
shapiro_res <- shapiro.test(mydata$PairDifferences)
shapiro_table <- tidy(shapiro_res) %>%
  select(statistic, p.value, method) %>%
  gt() %>%
  gt_theme_538() %>%
  tab_header(title = "Normality Test (Shapiro-Wilk)") %>%
  fmt_number(columns = c(statistic, p.value), decimals = 4) %>%
  tab_source_note(
    source_note = "If the P.VALUE is 0.05 or less, the number of pairs is fewer than 40, and the distribution of pair differences shows obvious non-normality or outliers, consider using the Wilcoxon Signed Rank Test results instead of the Paired-Samples t-Test results."
  )

# ============================================================
#  Reshape Data for Repeated-Measures Plot
# ============================================================
df_long <- mydata %>%
  pivot_longer(cols = c(V1, V2),
               names_to = "Measure",
               values_to = "Value")

# ============================================================
#  Repeated-Measures Boxplot (Interactive, with Means)
# ============================================================
group_means <- df_long %>%
  group_by(Measure) %>%
  summarise(mean_value = mean(Value), .groups = "drop")

boxplot_measures <- plot_ly() %>%
  add_trace(
    data = df_long,
    x = ~Measure, y = ~Value,
    type = "box",
    boxpoints = "outliers",   
    marker = list(color = "red", size = 4),
    line = list(color = "black"),
    fillcolor = "royalblue",
    name = ""
  ) %>%
  add_trace(
    data = group_means,
    x = ~Measure, y = ~mean_value,
    type = "scatter", mode = "markers",
    marker = list(
      symbol = "diamond", size = 9,
      color = "black", line = list(color = "white", width = 1)
    ),
    text = ~paste0("Mean = ", round(mean_value, 2)),
    hoverinfo = "text",
    name = "Group Mean"
  ) %>%
  layout(
    title = "Boxplot of Repeated Measures (V1 vs V2) with Means",
    xaxis = list(title = "Measure"),
    yaxis = list(title = "Value"),
    showlegend = FALSE
  )

# ============================================================
#  Parametric Test (Paired-Samples t-Test)
# ============================================================
t_res <- t.test(mydata$V2, mydata$V1, paired = TRUE)
t_table <- tidy(t_res) %>%
  select(statistic, parameter, p.value, conf.low, conf.high, method) %>%
  gt() %>%
  gt_theme_538() %>%
  tab_header(title = "Paired-Samples t-Test") %>%
  fmt_number(columns = c(statistic, p.value, conf.low, conf.high), decimals = 4)

t_summary <- mydata %>%
  select(V1, V2) %>%
  summarise_all(list(Mean = mean, SD = sd)) %>%
  gt() %>%
  gt_theme_538() %>%
  tab_header(title = "Group Means and SDs (t-Test)") %>%
  fmt_number(columns = everything(), decimals = 3)

# ============================================================
#  Nonparametric Test (Wilcoxon Signed Rank)
# ============================================================
wilcox_res <- wilcox.test(mydata$V1, mydata$V2, paired = TRUE,
                          exact = FALSE)
wilcox_table <- tidy(wilcox_res) %>%
  select(statistic, p.value, method) %>%
  gt() %>%
  gt_theme_538() %>%
  tab_header(title = "Wilcoxon Signed Rank Test") %>%
  fmt_number(columns = c(statistic, p.value), decimals = 4)

wilcox_summary <- mydata %>%
  select(V1, V2) %>%
  summarise_all(list(Mean = mean, SD = sd)) %>%
  gt() %>%
  gt_theme_538() %>%
  tab_header(title = "Group Means and SDs (Wilcoxon)") %>%
  fmt_number(columns = everything(), decimals = 3)

# ============================================================
#  Results Summary (in specified order)
# ============================================================
hist_plot
desc_table
shapiro_table
boxplot_measures
t_table
t_summary
wilcox_table
wilcox_summary