Rationale

Second-level agenda-setting theory extends first-level agenda-setting by examining not only which issues receive attention in media coverage but also which attributes of those issues are emphasized. In this way, second-level agenda-setting moves beyond issue salience to explore attribute salience — how the media frame and characterize a topic to influence public perceptions of it. Because news organizations face limits on time, space, and resources, editorial decisions often involve emphasizing certain aspects of an issue while minimizing others. These editorial choices can shape the public’s understanding by highlighting which facets of an issue appear most important.

Drawing on this framework, this project compares how Associated Press (APNews.com) coverage balances two contrasting perspectives within its immigration reporting: enforcement-oriented stories focusing on immigration arrests and deportations, and humanitarian-oriented stories focusing on immigrant rights and protections. By comparing weekly coverage volumes of these two attribute frames — immigration arrest versus immigration rights — from January 1 through September 30, 2025, the analysis offers insights into how differing immigration-related frames compete for attention on the media agenda.

The results enhance theoretical understanding of how attribute salience operates in media coverage of socially and politically charged issues like immigration. They also reveal whether enforcement- or rights-oriented frames received more consistent prominence in APNews.com coverage over time.

Hypothesis

Weekly APNews.com coverage volume of immigration arrest–related stories and immigrant rights–related stories differed during the first nine months of 2025.

Variables & Methods

Weekly APNews.com coverage volume of immigration-related stories served as the dependent variable. It was measured continuously as the number of stories published per week. The independent variable was “story attribute frame,” measured categorically as either “Immigrant Arrest” or “Immigrant Rights.”

Stories about immigration arrests were identified by enforcement-oriented phrases such as “immigration enforcement,” “deportation,” “ICE arrests,” and “border patrol.”

Stories emphasizing immigrant rights were identified by humanitarian or protection-oriented terms such as “asylum,” “DACA,” “refugee,” and “migrant protection.”

The analysis began by filtering APNews data to include only stories containing general immigration-related keywords such as immigration, immigrant, DACA, asylum, deportation, ICE, border patrol, or refugee. Within that filtered dataset, stories were flagged based on the enforcement or rights-oriented phrases above.

A paired-samples t-test assessed whether mean weekly coverage volume differed significantly between the two frames.

Result & Discussions

The figure below summarizes the weekly coverage volume of the Immigrant Arrest and Immigrant Rights frames across the study period. For much of the January–September 2025 period, APNews.com coverage of immigration arrests appeared more frequently than coverage emphasizing immigrant rights.

The most pronounced enforcement-related coverage occurred during Week 26 (June 22–27), when the number of immigration arrest–related stories peaked. Inspection of the stories from that week revealed heavy coverage of large-scale Immigration and Customs Enforcement (ICE) operations targeting undocumented workers across multiple states. This spike in enforcement coverage coincided with a noticeable dip in rights-related coverage.

By contrast, Week 4 (January 27–February 2) marked a high point in Immigrant Rights coverage, driven largely by AP reports on renewed debates over DACA protections and humanitarian responses to migrant family separations. During that same week, coverage of immigration arrests declined, suggesting a possible trade-off between enforcement and rights-oriented framing.

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.

On average, weekly APNews.com coverage featured approximately 8 “Immigrant Arrest” stories and 5 “Immigrant Rights” stories, a mean difference of about 3 articles per week. While the distribution of paired differences did not perfectly meet normality assumptions according to the Shapiro-Wilk test (p < 0.05), the dataset’s case count (39 paired observations) made a paired-sample t-test appropriate for assessing statistical significance.

Below is a box plot of weekly article volumes for Immigration Rights (V1) and Immigration Arrest (V2), followed by the results of the paired-samples t-test.

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

The significant t-test result (p < 0.01) supported the hypothesis that weekly APNews.com coverage volume of Immigrant Arrest and Immigrant Rights stories differed during the first nine months of 2025.

Overall, the results suggest that while APNews.com consistently covered both enforcement and rights-oriented aspects of immigration, enforcement-related stories received greater overall prominence. This pattern aligns with second-level agenda-setting predictions that certain attributes — particularly those tied to control, security, or enforcement — may receive more frequent emphasis, thereby influencing public perceptions of immigration as primarily a law enforcement issue rather than a humanitarian concern.

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