Code for Moss

Hi, Hannah:

Give this code a try. It will download KelceMahomes.rds, a data file containing 8,987 articles that mention either Travis Kelce or Patrick Mahomes (or, in probably many cases, both) and that were published between Jan. 1, 2025 and Nov. 16, 2025 by any of the following online news outlets:

espn.com
sports.yahoo.com
cbssports.com
apnews.com
usatoday.com
nytimes.com
washingtonpost.com

The specific Brandwatch query I used was:

(“Travis Kelce” OR “Patrick Mahomes”) AND (site:espn.com OR site:sports.yahoo.com OR site:cbssports.com OR site:apnews.com OR site:usatoday.com OR site:nytimes.com OR site:washingtonpost.com)

So, basically, the query picks up more stories about the two players by focusing on outlets that produce more sports-related content than APNews.com does all by itself. That additional volume gives you a lot more content to work with. I’ve peeked; if you do a basic first-level agenda-setting analysis comparing the volume of stories mentioning Kelce with the volume of stories mentioning Mahomes, Mahomes wins by something like three to one on average.

# ============================================
# APNews text analysis (First-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/KelceMahomes.rds"))
# Save the data on your computer
saveRDS(FetchedData, file = "KelceMahomes.rds")
# remove the downloaded data from the environment
rm (FetchedData)

APNews <- readRDS("KelceMahomes.rds")

# ============================================
# --- Flag Topic1-related stories ---
# ============================================

# --- Define Topic1 phrases ---
phrases <- c(
  "Kelce"
)

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

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

# --- Apply matching to flag Topic1 stories ---
APNews <- APNews %>%
  mutate(
    Full.Text.clean = str_squish(Full.Text),  # normalize whitespace
    Topic1 = if_else(
      str_detect(Full.Text.clean, regex(pattern, ignore_case = TRUE)),
      "Yes",
      "No"
    )
  )

# ============================================
# --- Flag Topic2-related stories ---
# ============================================

# --- Define Topic2 phrases ---
phrases <- c(
  "Mahomes"
)

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

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

# --- Apply matching to flag Topic2 stories ---
APNews <- APNews %>%
  mutate(
    Full.Text.clean = str_squish(Full.Text),
    Topic2 = if_else(
      str_detect(Full.Text.clean, regex(pattern, ignore_case = TRUE)),
      "Yes",
      "No"
    )
  )

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

# --- Load plotly if needed ---
if (!require("plotly")) install.packages("plotly")
library(plotly)

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

# --- Summarize weekly counts for Topic2 = "Yes" ---
Topic2_weekly <- APNews %>%
  filter(Topic2 == "Yes") %>%
  group_by(Week) %>%
  summarize(Count = n(), .groups = "drop") %>%
  mutate(Topic = "Mahomes") # <====== 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 ---
AS1 <- 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 AP News Articles",
    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 ---
# ============================================

AS1