Citizens’ evaluations of Trump tweets

Tutorial by: J.Zilinsky (TUM)

Introduction

In this exercise, we will load a dataset of YouGov sentiment ratings of Trump tweets and explore how different people rate those tweets.

Structure

The underlying data consists of tweets
So we could do text analysis
We will mostly focus here on visualizing numerical ratings
(But each word can of course be considered a variable!)

By the end of this slide deck, students should be comfortable:

Loading JSON data into R
Calculating summary statistics (means, proportions)
Transforming data (wide to long)
Visualizing distributions and comparisons using ggplot2
Highlighting specific observations in plots

Loading Libraries and Data

First, we load the necessary libraries. jsonlite lets us read JSON data directly from a URL, and tidyverse provides a suite of data manipulation and visualization tools.

library(jsonlite)
library(tidyverse)

# Load the YouGov tweet sentiment data from GitHub
yg <- fromJSON("https://github.com/kennyjoseph/trump_tweets_icwsm/raw/master/data/all_yougov_tweetdata_updated.json")

Check the structure of the loaded object

names(yg)

[1] "text"             "group"            "data"             "created_at"      
[5] "survey_date"      "objectID"         "_highlightResult"

# View first 5 tweet texts
yg$text[1:5]

[1] "Great honor to be headed to the Army-Navy game today. Will be there shortly, landing now! https://t.co/ByAfESq8aS"                                                                                                                                                                       
[2] "....left and right, he then woke up from his dream screaming that HE LIED. Next time I go to Vietnam I will ask 'the Dick' to travel with me!"                                                                                                                                           
[3] "Watched Da Nang Dick Blumenthal on television spewing facts almost as accurate as his bravery in Vietnam (which he never saw). As the bullets whizzed by Da Nang Dicks head, as he was saving soldiers...."                                                                              
[4] "Very sad day & night in Paris. Maybe it's time to end the ridiculous and extremely expensive Paris Agreement and return money back to the people in the form of lower taxes? The U.S. was way ahead of the curve on that and the only major country where emissions went down last year!"
[5] "'This is collusion illusion, there is no smoking gun here. At this late date, after all that we have gone through, after millions have been spent, we have no Russian Collusion. There is nothing impeachable here.' @GeraldoRivera Time for the Witch Hunt to END!"

# Peek at the nested data frame
yg$data %>% tibble() %>% head()

# A tibble: 6 × 4
  All$score $base $data     Democrat$score Republican$score Independent$score
      <dbl> <int> <list>             <dbl>            <dbl>             <dbl>
1      20.6  1000 <dbl [5]>          -38.2            104.              37.3 
2     -58.3  1000 <dbl [5]>         -140.              45.1            -48.0 
3     -58.3  1000 <dbl [5]>         -140.              45.1            -48.0 
4     -26.7  1000 <dbl [5]>         -123.              92.1             -3.53
5     -34.8  1000 <dbl [5]>         -133.              99.1            -22.8 
6      13.5  1000 <dbl [5]>          -54.3            110.              29.5 
# ℹ 6 more variables: Democrat$base <int>, $data <list>, Republican$base <int>,
#   $data <list>, Independent$base <int>, $data <list>

Creating a Tidy Data Frame

We now construct a tidy tibble that includes:

text: the tweet content
score: overall sentiment score
score_dems: average rating by Democrats
score_reps: average rating by Republicans

data <- tibble(
  text = yg$text,
  score = yg$data$All$score,
  score_dems = yg$data$Democrat$score,
  score_reps = yg$data$Republican$score
)

# Display the first few rows
data

# A tibble: 4,404 × 4
   text                                              score score_dems score_reps
   <chr>                                             <dbl>      <dbl>      <dbl>
 1 Great honor to be headed to the Army-Navy game t…  20.6      -38.2      104. 
 2 ....left and right, he then woke up from his dre… -58.3     -140.        45.1
 3 Watched Da Nang Dick Blumenthal on television sp… -58.3     -140.        45.1
 4 Very sad day & night in Paris. Maybe it's time t… -26.7     -123.        92.1
 5 'This is collusion illusion, there is no smoking… -34.8     -133.        99.1
 6 ....I am thankful to both of these incredible me…  13.5      -54.3      110. 
 7 I am pleased to announce my nomination of four-s…  13.5      -54.3      110. 
 8 AFTER TWO YEARS AND MILLIONS OF PAGES OF DOCUMEN… -39.6     -136.        78.2
 9 The idea of a European Military didn't work out … -18.2     -109.        87.2
10 The Paris Agreement isn't working out so well fo… -42.2     -131.        69.6
# ℹ 4,394 more rows

Summary Statistics by Party

Check for missing values:

any(is.na(data$score_dems))  # Were there any missing Democrat ratings?

[1] FALSE

any(is.na(data$score_reps))  # Were there any missing Republican ratings?

[1] FALSE

Calculate the average rating among Democrats and Republicans:

mean_dems <- data$score_dems %>% mean(na.rm = TRUE)
mean_reps <- data$score_reps %>% mean(na.rm = TRUE)

mean_dems  # Average rating by Democrats

[1] -93.17322

mean_reps  # Average rating by Republicans

[1] 100.5169

A quick look at the data

What were the most popular tweets?
What is the range of the data?

# Arrange tweets by descending and ascending overall score
# Note this will not influence the original data, or subsequent plots
data %>% arrange(score) %>% head()

# A tibble: 6 × 4
  text                                               score score_dems score_reps
  <chr>                                              <dbl>      <dbl>      <dbl>
1 I know Mark Cuban well. He backed me big-time but… -81.4      -134.     -18.0 
2 I heard poorly rated @Morning_Joe speaks badly of… -77.5      -143.       6.39
3 ...to Mar-a-Lago 3 nights in a row around New Yea… -77.5      -143.       6.39
4 Crazy Joe Scarborough and dumb as a rock Mika are… -76.7      -150.      27.7 
5 Lebron James was just interviewed by the dumbest … -72.3      -168.      45.8 
6 Just watched Wacky Tom Steyer, who I have not see… -71.7      -148.      45.9

data %>% 
 summarize(lowest = min(score),highest=max(score))

# A tibble: 1 × 2
  lowest highest
   <dbl>   <dbl>
1  -81.4    82.9

Exploring Overall Sentiment Distribution

# Histogram of overall sentiment scores
data %>%
  ggplot(aes(x = score)) +
  geom_histogram(bins = 35, fill = "yellow3", color = "black") +
  theme_bw() +
  labs(title = "Distribution of Overall Sentiment Scores", x = "Sentiment Score", y = "Count")

Exploring Overall Sentiment Distribution

# Density plot of overall scores
data %>%
  ggplot(aes(x = score)) +
  geom_density() +
  theme_bw() +
  labs(title = "Density of Overall Sentiment Scores", x = "Sentiment Score", y = "Density")

Breaking down the data by group

Compute and compare mean ratings by party

What should the structure of the input be?

data %>%
  summarize(
    Democrats = mean(score_dems, na.rm = TRUE),
    Republicans = mean(score_reps, na.rm = TRUE)
  )

# A tibble: 1 × 2
  Democrats Republicans
      <dbl>       <dbl>
1     -93.2        101.

Breaking down the data by group

Compute and compare mean ratings by party

What should the structure of the input be?

Better to stack the data (go from wide to long)

data %>%
  summarize(
    Democrats = mean(score_dems, na.rm = TRUE),
    Republicans = mean(score_reps, na.rm = TRUE)
  ) %>% pivot_longer(cols = everything(), 
                 names_to = "party", 
                 values_to = "mean_score")

# A tibble: 2 × 2
  party       mean_score
  <chr>            <dbl>
1 Democrats        -93.2
2 Republicans      101.

Preparing a simple bar plot

data %>%
  summarize(
    Democrats = mean(score_dems, na.rm = TRUE),
    Republicans = mean(score_reps, na.rm = TRUE)
  ) %>%
  pivot_longer(cols = everything(), names_to = "party", values_to = "mean_score") %>%
  ggplot(aes(x = party, y = mean_score)) +
  geom_col(aes(fill = party), width = 0.3) +
  geom_hline(yintercept = 0, color = "black") +
  scale_fill_manual(values = c("Democrats" = "blue3", "Republicans" = "red3")) +
  theme_bw(base_size = 12) +
  labs(
    x = "Party",
    y = "Mean Sentiment Score",
    title = "Mean Sentiment Score by Party (All Tweets)",
    fill = "Party"
  )

Breaking down the data by group

Scatterplot of Party Ratings Against Each Other

Plot Democrat ratings (score_dems) against Republican ratings (score_reps) to see whether respondents tend to agree (in spite of an anticipate “intercept shift”)

fig_layer <- data %>%
  ggplot(aes(x = score_reps, y = score_dems))

fig_layer + 
 geom_point()

Ratings by party

Another way to show the correlation

fig_layer +
  geom_smooth(method = "lm") +
  theme_bw() +
  labs(
    title = "Relationship Between Republican and Democrat Ratings",
    x = "Republican Rating",
    y = "Democrat Rating"
  )

Comparing Distributions of Ratings

Reshape the full data for plotting density curves of each rating type:

( long_data <- data %>%
  pivot_longer(
    cols = c(score, score_dems, score_reps),
    names_to = "party",
    values_to = "score"
  ) )

# A tibble: 13,212 × 3
   text                                                             party  score
   <chr>                                                            <chr>  <dbl>
 1 Great honor to be headed to the Army-Navy game today. Will be t… score   20.6
 2 Great honor to be headed to the Army-Navy game today. Will be t… scor…  -38.2
 3 Great honor to be headed to the Army-Navy game today. Will be t… scor…  104. 
 4 ....left and right, he then woke up from his dream screaming th… score  -58.3
 5 ....left and right, he then woke up from his dream screaming th… scor… -140. 
 6 ....left and right, he then woke up from his dream screaming th… scor…   45.1
 7 Watched Da Nang Dick Blumenthal on television spewing facts alm… score  -58.3
 8 Watched Da Nang Dick Blumenthal on television spewing facts alm… scor… -140. 
 9 Watched Da Nang Dick Blumenthal on television spewing facts alm… scor…   45.1
10 Very sad day & night in Paris. Maybe it's time to end the ridic… score  -26.7
# ℹ 13,202 more rows

long_data %>%
  ggplot(aes(y = party, x = score)) +
  geom_point(alpha = .1) + labs(x="Tweet ratings",y="")

long_data %>%
  ggplot(aes(y = party, x = score)) +
  geom_jitter(alpha = .1) + labs(x="Tweet ratings",y="")

long_data %>%
  ggplot(aes(y = party, x = score)) +
  geom_jitter(alpha = .05,height = .2) + labs(x="Tweet ratings",y="")

long_data %>%
 mutate(Party = case_when(
    party == "score_dems" ~ "Democrats",
    party == "score_reps" ~ "Republicans",
    TRUE ~ "Overall") ) %>%
  ggplot(aes(y = Party, x = score)) +
  geom_jitter(alpha = .05,height = .2) + labs(x="Tweet ratings",y="")

long_data %>%
 mutate(Party = case_when(
    party == "score_dems" ~ "Among Democrats",
    party == "score_reps" ~ "Among Republicans",
    TRUE ~ "Overall") ) %>%
  ggplot(aes(y = Party, x = score, color = Party)) +
  geom_jitter(alpha = .065,height = .2, show.legend = F) + labs(x="Tweet ratings",y="") +
 scale_color_manual(values = c("Among Democrats" = "steelblue", "Among Republicans" = "red4", "score"= "Overall")) + theme_minimal()

Other ways to show distributions of numerical variables

long_data %>%
 mutate(Party = case_when(
    party == "score_dems" ~ "Among Democrats",
    party == "score_reps" ~ "Among Republicans",
    TRUE ~ "Overall") ) %>%
  ggplot(aes(y = Party, x = score, fill = Party)) +
  geom_boxplot() +
 scale_fill_manual(values = c("Among Democrats" = "steelblue", "Among Republicans" = "red4", "score"= "Overall")) + theme_minimal()

long_data %>%
 mutate(Party = case_when(
    party == "score_dems" ~ "Among Democrats",
    party == "score_reps" ~ "Among Republicans",
    TRUE ~ "Overall") ) %>%
  ggplot(aes(y = Party, x = score, fill = Party)) +
  geom_boxplot(show.legend = FALSE) +
 scale_fill_manual(values = c("Among Democrats" = "steelblue", "Among Republicans" = "red4", "score"= "Overall")) + theme_minimal()

You can also add a jitter layer to the boxplot to show individual data points:

long_data %>%
 mutate(Party = case_when(
    party == "score_dems" ~ "Among Democrats",
    party == "score_reps" ~ "Among Republicans",
    TRUE ~ "Overall") ) %>%
  ggplot(aes(y = Party, x = score, fill = Party)) +
 geom_jitter(alpha = .065,height = .2, show.legend = F) + labs(x="Tweet ratings",y="") +
  geom_boxplot(show.legend = FALSE) +
 scale_fill_manual(values = c("Among Democrats" = "steelblue", "Among Republicans" = "red4", "score"= "Overall")) + theme_minimal()

Adjust the boxplot transparency:

long_data %>%
 mutate(Party = case_when(
    party == "score_dems" ~ "Among Democrats",
    party == "score_reps" ~ "Among Republicans",
    TRUE ~ "Overall") ) %>%
  ggplot(aes(y = Party, x = score, fill = Party)) +
 geom_jitter(alpha = .065,height = .2, show.legend = F) + labs(x="Tweet ratings",y="") +
  geom_boxplot(show.legend = FALSE,alpha=.6) +
 scale_fill_manual(values = c("Among Democrats" = "steelblue", "Among Republicans" = "red4", "score"= "Overall")) + theme_minimal()

Make the points more subtle

long_data %>%
 mutate(Party = case_when(
    party == "score_dems" ~ "Among Democrats",
    party == "score_reps" ~ "Among Republicans",
    TRUE ~ "Overall") ) %>%
  ggplot(aes(y = Party, x = score, fill = Party)) +
 geom_jitter(alpha = .045,height = .1, size=.25,show.legend = F) + labs(x="Tweet ratings",y="") +
  geom_boxplot(show.legend = FALSE,alpha=.6) +
 scale_fill_manual(values = c("Among Democrats" = "steelblue", "Among Republicans" = "red4", "score"= "Overall")) + theme_minimal()

Make the boxes narrower and the border thinner:

long_data %>%
 mutate(Party = case_when(
    party == "score_dems" ~ "Among Democrats",
    party == "score_reps" ~ "Among Republicans",
    TRUE ~ "Overall") ) %>%
  ggplot(aes(y = Party, x = score, fill = Party)) +
 geom_jitter(alpha = .045,height = .1, size=.25,show.legend = F) + labs(x="Tweet ratings",y="") +
  geom_boxplot(alpha=.75,size=.2,width=.3,show.legend = FALSE,) +
 scale_fill_manual(values = c("Among Democrats" = "steelblue", "Among Republicans" = "red4", "score"= "Overall")) + theme_minimal()

Adjust the dot colors as well:

long_data %>%
 mutate(Party = case_when(
    party == "score_dems" ~ "Among Democrats",
    party == "score_reps" ~ "Among Republicans",
    TRUE ~ "Overall") ) %>%
  ggplot(aes(y = Party, x = score, fill = Party, color = Party)) +
 geom_jitter(alpha = .045,height = .1, size=.25,show.legend = F) + labs(x="Tweet ratings",y="") +
  geom_boxplot(alpha=.75,size=.2,width=.3,show.legend = FALSE,) +
 scale_color_manual(values = c("Among Democrats" = "steelblue", "Among Republicans" = "red4", "score"= "Overall")) + scale_fill_manual(values = c("Among Democrats" = "steelblue", "Among Republicans" = "red4", "score"= "Overall")) + theme_minimal()

Make final adjustments:

long_data %>%
 mutate(Party = case_when(
    party == "score_dems" ~ "Among Democrats",party == "score_reps" ~ "Among Republicans",TRUE ~ "Overall"), Party = factor(Party,levels = c("Among Republicans","Overall","Among Democrats"))) %>%
  ggplot(aes(y = Party, x = score, fill = Party, color = Party)) +
 geom_jitter(alpha = .045,height = .1, size=.25,show.legend = F) + labs(x="Tweet ratings",y="") +
  geom_boxplot(outliers=F,alpha=.75,size=.2,width=.3,show.legend = FALSE,) +
 scale_color_manual(values = c("Among Democrats" = "steelblue", "Among Republicans" = "red4", "score"= "Overall")) + scale_fill_manual(values = c("Among Democrats" = "steelblue", "Among Republicans" = "red4", "score"= "Overall")) + theme_minimal() + ggtitle("Ratings of Trump tweets among YouGov respondents")

Still more ways to show distributions

long_data %>%
  ggplot(aes(x = score, color = party)) +
  geom_density() +
  theme_bw() + labs(title = "Density of Scores by Rating Type", x = "Score", y = "Density")

long_data %>%
  ggplot(aes(x = score, fill = party)) +
  geom_density(alpha = 0.6, show.legend = TRUE) +
  scale_fill_manual(
    values = c("score_dems" = "steelblue", "score_reps" = "red4", "score" = "grey")
  ) +
  theme_bw(base_size = 12) +
  labs(title = "Overlayed Density of Scores", x = "Score", y = "Density", fill = "Rating Type")

long_data %>%
  ggplot(aes(x = score, fill = party)) +
  geom_histogram(alpha = 0.5, color = "black", bins = 30) +
  scale_fill_brewer(palette = "Set1", direction = -1) +
  theme_bw() +
  labs(title = "Histogram of Scores by by Party ID", x = "Score", y = "Count", fill = "Rating Type")

Content classification

Identifying Tweets that Mention Obama

We create a binary indicator obama which is 1 if the tweet text contains the word “Obama”, otherwise 0. Then we can compute how often tweets mention Obama.

# Create indicator for "Obama"
data$obama <- ifelse(str_detect(data$text, "Obama"), 1, 0)

# Frequency of Obama mentions
table(data$obama)


   0    1 
4216  188

# Percentage of tweets that mention Obama
prop_obama <- data$obama %>% mean()
prop_obama

[1] 0.04268847

Comparing Ratings for Obama-Mentioning Tweets

Compute mean sentiment ratings by Democrats and Republicans for tweets that mention Obama:

data %>%
  filter(obama == 1) %>%
  summarize(
    mean_dems = mean(score_dems, na.rm = TRUE),
    mean_reps = mean(score_reps, na.rm = TRUE)
  )

# A tibble: 1 × 2
  mean_dems mean_reps
      <dbl>     <dbl>
1     -127.      90.8

Comparing Ratings for Obama-Mentioning Tweets

Next, compare average ratings between tweets that do and do not mention Obama:

data %>%
  group_by(obama) %>%
  summarize(
    mean_dems = mean(score_dems, na.rm = TRUE),
    mean_reps = mean(score_reps, na.rm = TRUE)
  )

# A tibble: 2 × 3
  obama mean_dems mean_reps
  <dbl>     <dbl>     <dbl>
1     0     -91.6     101. 
2     1    -127.       90.8

Make a nicer table with descriptive labels and rounded values:

tab <- data %>%
  group_by(obama) %>%
  summarize(
    mean_dems = mean(score_dems, na.rm = TRUE),
    mean_reps = mean(score_reps, na.rm = TRUE)
  ) %>%
  mutate(obama = ifelse(obama == 1, "Obama Mentioned", "Obama Not Mentioned")) %>%
  rename(
    "Tweet type" = obama,
    "Avg. rating among Democrats" = mean_dems,
    "Avg. rating among Republicans" = mean_reps
  )

# Display the table
(tab %>% knitr::kable(digits = 1))

Tweet type	Avg. rating among Democrats	Avg. rating among Republicans
Obama Not Mentioned	-91.6	100.9
Obama Mentioned	-127.4	90.8

Highlighting Obama-Mention Tweets in Scatterplot

Identify tweets that mention Obama in the scatter of party ratings:

data %>%
  ggplot(aes(x = score_reps, y = score_dems)) +
  geom_point(aes(color = factor(obama)), size = 0.4) +
  scale_color_manual(values = c("0" = "grey60", "1" = "orange")) +
  theme_bw(base_size = 12) + labs(title = "Party Ratings with Obama-Mention Tweets Highlighted",x = "Republican Rating",y = "Democrat Rating",color = "Obama Mentioned")

Highlighting Obama-Mention Tweets in Scatterplot

If desired, use gghighlight to emphasize only the Obama-mentioning tweets:

library(gghighlight)
data %>%
  ggplot(aes(x = score_reps, y = score_dems)) +
  geom_point(aes(color = obama), size = 0.4,show.legend=F) +
  gghighlight(obama == 1) + theme_bw(base_size = 12) + labs(subtitle = "Highlighted tweets mention Barack Obama", color = "Obama Mention")

Showing the full text of tweets

Pulling Out Specific Tweets

We can hand-pick some observations and display the full text of the tweets which were rated
For example, here select tweets from rows 19 through 23 and display their texts along with their ratings:

data[19:22, ] %>%
  ggplot(aes(y = text)) +
  geom_point(aes(x = score_dems), color = "blue3") +
  geom_point(aes(x = score_reps), color = "red3") +
  scale_y_discrete(labels = scales::label_wrap(50)) +
  labs(x = "Rating", title = "Ratings of Selected Tweets")

data[60:63, ] %>%
  ggplot(aes(y = text)) +
  geom_col(aes(x = score_dems), fill = "blue3",width=.5) +
  geom_col(aes(x = score_reps), fill = "red3",width=.5) +
  scale_y_discrete(labels = scales::label_wrap(50)) +
  labs(x = "Rating", title = "Ratings of Selected Tweets",y="")

Finding unusual / surprising observations

Let’s see if there are tweets mentioning Obama with POSITIVE Democrat ratings

# Extract tweets mentioning Obama with positive Democrat ratings
data %>%
  filter(obama == 1, score_dems >= 0) %>% nrow()

[1] 1

So, there was one such tweet! What did it say?

# Extract tweets mentioning Obama with positive Democrat ratings
data %>%
  filter(obama == 1, score_dems >= 0) %>% pull(text)

[1] "I agree with President Obama 100%! https://t.co/PI3aW1Zh5Q"

Conclusion

Students should now be able to:

Load JSON data into R and inspect its structure
Calculate and interpret summary statistics by group
Create and format tables in markdown
Reshape data between wide and long formats
Visualize distributions and relationships with ggplot2
Highlight specific observations in visualizations

Feel free to modify the code and experiment with different subsets or additional variable transformations. Good luck!