Brief Text Analysis

setwd("/Users/isaiahmireles/Desktop/Trump folder")
population <- read.csv("trump_tweets_dataset.csv")
df <- read.csv("trump_sample_labeled.csv")

Research Qs)

• How does Donald Trump’s public discourse distribute across core themes (e.g., immigration, religion, education, economics), and how does this thematic composition evolve over time (2009–2026)?

• What are the beliefs of trump about each topic?

• Which themes are most strongly associated with viral engagement (likes, retweets, replies), and does this relationship change over time?

• Does emotionally charged rhetoric within certain themes (e.g., immigration + negative sentiment) disproportionately drive engagement? Provide Examples

Brief

Most to least confident education, theme_label

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

edu <- 
  df |> select(date, theme_label, confidence, text, post_url) |> 
  filter(theme_label=="education") |> 
  arrange(desc(confidence))

edu_conf <- edu |> filter(confidence>=.3)
colnames(df)

##  [1] "date"           "platform"       "handle"         "text"          
##  [5] "favorite_count" "repost_count"   "deleted_flag"   "word_count"    
##  [9] "hashtags"       "urls"           "user_mentions"  "media_count"   
## [13] "media_urls"     "post_url"       "text_lwr"       "text_clean"    
## [17] "theme_label"    "confidence"

Vis.

library(dplyr)
library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

df <- 
  df |> 
  mutate(date = as.Date(date))

df_monthly <- 
  df |> 
  mutate(month = floor_date(date, unit = "month"))

topic_monthly_counts <- 
  df |> 
  mutate(month = floor_date(date, "month")) |> 
  group_by(month, theme_label) |> 
  summarize(
    n_posts = n(),
    .groups = "drop"
  )

topic_monthly_prop <- 
  df |> 
  mutate(month = floor_date(date, "month")) |> 
  group_by(month, theme_label) |> 
  summarize(n_posts = n(), .groups = "drop") |> 
  group_by(month) |> 
  mutate(prop = n_posts / sum(n_posts)) |> 
  ungroup()

df_high_conf <- 
  df |> 
  filter(confidence >= 0.3)

topic_monthly_counts <- 
  df_high_conf |> 
  mutate(month = floor_date(date, "month")) |> 
  group_by(month, theme_label) |> 
  summarize(n_posts = n(), .groups = "drop")

library(ggplot2)

topic_monthly_counts |> 
  ggplot(aes(x = month, y = n_posts, color = theme_label)) +
  geom_line() +
  labs(
    title = "Tweets by Theme Over Time",
    x = "Month",
    y = "Number of Tweets"
  )

Data was got from

GDP <- read.csv("GDP.csv")
GDP$observation_date <- as.Date(GDP$observation_date)
GDP <- GDP |> arrange(desc(observation_date))
df <- df |> arrange(desc(date))

unique(df$theme_label)

## [1] "economics"    "education"    "immigration"  "religion"     "homelessness"

Updated-Research Qs)

Government Data (

• BEA: Quarterly GDP

• BLS: Monthly Unemployment Rate, CPI

• Census: Annual Median Income, Poverty Rate

• HUD: Annual Homelessness Counts

• DHS: Monthly Border Encounters

• NCES: Annual Education Statistics

General Questions

• Does tweet sentiment shift significantly in response to changes in official economic indicators (GDP, unemployment, CPI)?

• Are tweets posted during periods of worsening government indicators associated with higher mean engagement?

• Is emotionally extreme rhetoric (high absolute sentiment score) consistently associated with higher virality across themes?

Economics

• Does sentiment in economic tweets align directionally with changes in GDP, unemployment, CPI, income, and poverty at the time of posting?

Immigration

• Does the frequency and negativity of immigration tweets increase during months with higher DHS border encounter totals?

Homelessness

• Do homelessness tweets increase or become more negative when HUD homelessness counts and housing CPI rise?

Education

• Does sentiment in education tweets correspond with changes in NCES education funding or student loan statistics?

Religion

• Is religious tweet sentiment stable over time, or does it shift during periods of demographic change in Census religious affiliation data?