4-reddit-chatgpt-analysis.Rmd

---
title: "Generative AI Generating Buzz: Volume, Engagement, and Content of Initial Reactions to ChatGPT in Discussions Across Education-Related Subreddits"
author: "Bret Staudt Willet"
date: "2023-11-30"
output: html_document
---

--------------------------------------------------------------------------------
## Set Up
--------------------------------------------------------------------------------

```{r setup}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(RedditExtractoR)
library(anytime)
library(lubridate)
library(RColorBrewer)
library(viridis)
library(scales)
library(quanteda)
library(quanteda.textstats)
library(quanteda.textplots)
library(spacyr)
library(seriation)
```





--------------------------------------------------------------------------------
## ONE-TIME INITIAL SETUP
--------------------------------------------------------------------------------

```{r, include=FALSE, message=FALSE, eval=FALSE}
posts_dec_jan <- read_csv("./data-gpt-dec-jan/!filtered-posts.csv")
posts_feb_mar <- read_csv("./data-gpt-feb-mar/!filtered-posts.csv")
comments_dec_jan <- read_csv("./data-gpt-dec-jan/!filtered-comments.csv")
comments_feb_mar <- read_csv("./data-gpt-feb-mar/!filtered-comments.csv")

combined_posts0 <- bind_rows(posts_dec_jan, posts_feb_mar)
combined_comments0 <- bind_rows(comments_dec_jan, comments_feb_mar)

write_csv(combined_posts0, "./data-gpt/!combined-posts.csv")
write_csv(combined_comments0, "./data-gpt/!combined-comments.csv")
```










--------------------------------------------------------------------------------
## START HERE
--------------------------------------------------------------------------------

```{r, include=FALSE, message=FALSE, eval=FALSE}
combined_posts <- read_csv("./data-gpt/!combined-posts.csv") 
combined_comments <- read_csv("./data-gpt/!combined-comments.csv")

posts_names <- names(combined_posts)
comments_names <- names(combined_comments)
```

```{r, include=FALSE, message=FALSE}
posts_liwc <- 
  read_csv("./data-gpt/!combined-posts-with-liwc.csv")
names(posts_liwc)[1:length(posts_names)] <- posts_names
posts_liwc <- 
  posts_liwc %>%
  filter(subreddit != "subreddit") %>%
  mutate(num_comments = as.integer(num_comments),
         post_score = as.integer(score),
         upvote_ratio = as.double(upvote_ratio), 
         post_date = as.Date(post_date_time)
  )

comments_liwc0 <- 
  read_csv("./data-gpt/!combined-comments-with-liwc.csv", col_names = TRUE)
names(comments_liwc0)[1:length(comments_names)] <- comments_names

comments_liwc <- 
  comments_liwc0 %>% 
  rename(comment_date = date,
         comment_text = comment) %>%
  mutate(comment_score = as.integer(score),
        comment_date = as.Date(comment_date, "%m/%d/%y")) %>%
  select(-score)
```

```{r}
ren <- c(
  "r/education", "r/Teachers", "r/teachingresources", "r/edtech",
  "r/AdultEducation", "r/ArtEd", "r/CSEducation", "r/ECEProfessionals",
  "r/ELATeachers", "r/highereducation", "r/historyteachers", "r/itinerantteachers",
  "r/matheducation", "r/MusicEd", "r/ScienceTeachers", "r/slp",
  "r/specialed", "r/teacherspromote", "r/TeachersInTransition", "r/teaching",
  "r/Professors", "r/academia", "r/instructionaldesign", "r/TeacherTales", 
  "r/OnlineEducation"
)

#sub <- "education"
#RedditExtractoR::find_subreddits(sub) %>% filter(subreddit == sub) %>% pull(subscribers)

total_subscriber_n <-  # as of 4/5/2023
  c(166688, 419352, 32329, 18441,
    4155, 6173, 23733, 26200,
    15990, 52686, 12847, 2,
    24315, 15906, 37097, 37526,
    13635, 468, 6063, 108342,
    114965, 57860, 23641, 29919,
    13973
  )

total_subscriber_n_feb2023 <-  # as of 2/3/2023
  c(164749, 403896, 31648, 17958,
    4098, 5856, 23578, 24555,
    15632, 51991, 12607, 2,
    23929, 15645, 36317, 36388,
    12996, 453, 2912, 105219,
    112194, 55607, 22858, 29695,
    13743
  )

total_post_n_dec_jan <-  # as of 2/3/2023; covers Dec 2022 to Jan 2023
  c(1220, 6386, 321, 116,
    11, 95, 37, 560,
    167, 280, 167, 6,
    140, 143, 236, 1330,
    207, 23, 248, 1192,
    2074, 585, 370, 63,
    198
  )
total_post_n_feb_mar <-  # as of 4/4/2023; covers Feb to Mar 2023
  c(1170, 6912, 322, 142,
    13, 103, 40, 724,
    156, 394, 146, 0,
    142, 149, 279, 1468,
    330, 27, 648, 939,
    1725, 747, 356, 71,
    251
  )

total_post_n <- total_post_n_dec_jan + total_post_n_feb_mar

subreddit_overall_info <- 
  tibble(ren, total_subscriber_n, total_post_n) %>%
  rename(subreddit = ren) %>%
  arrange(subreddit)

subreddit_overall_info
```

## Summarize Posts and Comments

```{r}
subreddit_comment_info <-
  comments_liwc %>%
  group_by(subreddit) %>%
  summarize(comment_n = n(),
            comment_WC_median = median(WC),
            comment_score_median = median(comment_score)
            )

subreddit_post_info <-
  posts_liwc %>%
  group_by(subreddit) %>%
  summarize(post_gpt_n = n(),
            post_WC_median = median(WC),
            post_score_median = median(post_score),
            response_n = sum(num_comments > 0),
            response_rate = round(100 * response_n / post_gpt_n, 2),
            thread_length_median = median(num_comments),
            post_upvote_ratio_median = median(upvote_ratio)
            ) %>%
  left_join(subreddit_comment_info, by = 'subreddit')
```

```{r}
subreddit_info <-
  subreddit_overall_info %>%
  left_join(subreddit_post_info, by = 'subreddit') %>%
  replace(is.na(.), 0) %>%
  mutate(post_gpt_p = round(100 * post_gpt_n / total_post_n, 2)) %>%
  relocate(subreddit:post_gpt_n, post_gpt_p)
subreddit_info
```

```{r}
first_post <-
  posts_liwc %>% 
  group_by(subreddit) %>%
  summarize(earliest_gpt_date = min(date)) %>%
  arrange(earliest_gpt_date)
first_post
```

```{r}
most_comments <-
  posts_liwc %>% 
  group_by(subreddit) %>%
  summarize(max_comments = max(num_comments)) %>%
  arrange(desc(max_comments))
most_comments
```

```{r}
most_words_post <-
  posts_liwc %>% 
  group_by(subreddit) %>%
  summarize(max_words_post = max(WC)) %>%
  arrange(desc(max_words_post))
most_words_post
```

```{r}
most_words_comment <-
  comments_liwc %>% 
  group_by(subreddit) %>%
  summarize(max_words_comment = max(WC)) %>%
  arrange(desc(max_words_comment))
most_words_comment
```

```{r}
subreddit_info_full <-
  subreddit_info %>%
  full_join(first_post, by = "subreddit") %>%
  full_join(most_comments, by = "subreddit") %>%
  full_join(most_words_post, by = "subreddit") %>%
  full_join(most_words_comment, by = "subreddit") %>%
  mutate(across(where(is.numeric), \(x) coalesce(x, 0))) %>%
  arrange(desc(post_gpt_n))

write_csv(subreddit_info_full, "output/subreddit-info-full.csv")

subreddit_info_full
```

```{r}
subreddit_info_full %>%
  summarize(post_gpt_n = sum(post_gpt_n),
            comment_n = sum(comment_n))
```





################################################################################

```{r}
# See https://sashamaps.net/docs/resources/20-colors/
color_count <- length(unique(posts_liwc$subreddit))
my_colors <-
  c('#e6194b', '#3cb44b', '#ffe119', '#4363d8', '#f58231',
             '#911eb4', '#46f0f0', '#f032e6', '#bcf60c', '#fabebe', 
             '#008080', '#e6beff', '#9a6324', '#800000', '#000075',
             '#aaffc3', '#808000', '#ffd8b1', '#fffac8','#808080', 
             '#000000', '#ffffff')[1:color_count]
```


 
################################################################################
## Combining LIWC scores for Posts and Comments
################################################################################

```{r}
posts_subset <- 
  posts_liwc %>% 
  select(subreddit, post_date, title, post_text, WC:death) %>%
  mutate(type = "post", .after = subreddit) %>%
  rename(text = post_text,
         date = post_date) %>%
  filter(date <= as.Date("2023-03-31"))
comments_subset <- 
  comments_liwc %>% 
  select(subreddit, comment_date, comment_text, WC:death) %>%
  mutate(type = "comment", .after = subreddit) %>%
  rename(text = comment_text,
         date = comment_date) %>%
  filter(date <= as.Date("2023-03-31"))

shared_vars <- names(posts_subset)[c(1:3, 5:41)]

all_liwc <-
  posts_subset %>%
  full_join(comments_subset, by = shared_vars) %>%
  rename(`Analytical Thinking` = Analytic,
         Authenticity = Authentic,
         `Emotional Tone` = Tone)

all_liwc %>% group_by(type) %>% summarize(n())
```



################################################################################
## Posts and Comments Over Time
################################################################################


```{r}
all_days <- 
  unique(all_liwc$subreddit) %>%
  as_tibble() %>%
  rename(subreddit = value) %>%
  reframe(type = c("post", "comment"), .by = "subreddit") %>%
  reframe(date = seq.Date(as.Date("2022-11-30"), as.Date("2023-03-31"), by = "days"),
          .by = c("subreddit", "type")
  )

dates_by_type <- 
  all_liwc %>%
  add_count(date, type, subreddit) %>%
  select(date, type, subreddit, n) %>%
  distinct(date, type, subreddit, .keep_all = TRUE) %>%
  mutate(type = factor(type, levels = c("post", "comment")))
  #right_join(all_days, by = c("date", "type", "subreddit")) %>%
  #mutate(across(where(is.numeric), \(x) coalesce(x, 0)),
         #type_size = ifelse(type == "comment", 1.0, 2.0),
         #type_alpha = ifelse(type == "comment", 0.3, 0.8))
```

```{r, message=FALSE, warning=FALSE}
ggplot(data = dates_by_type, aes(x = date, y = n, group = type)) +
  geom_point(size = 2.0, alpha = 0.5, aes(color = type)) + 
  scale_color_manual(values = c(my_colors[15], my_colors[5]), name = "Type:") +
  facet_wrap(vars(subreddit), ncol = 4, scales = "fixed") +
  scale_x_date(breaks = scales::breaks_pretty(n = 5), date_minor_breaks = "1 month") +
  scale_y_continuous(breaks = scales::breaks_pretty(n = 3)) +
  xlab(NULL) +
  ylab("Daily Count") +
  theme_bw() +
  theme(legend.position = 'bottom',
        legend.title = element_text(size = 14, face = "bold"),
        legend.text = element_text(size = 12),
        legend.box.background = element_rect(colour = "black"),
        axis.title.y = element_text(size = 14),
        axis.text.y = element_text(size = 11),
        strip.text = element_text(size = 11),
        panel.spacing = unit(1.1, 'lines')) +
  guides(color = guide_legend(override.aes = list(alpha = 0.8, size = 5.0)))
```

```{r, eval=FALSE}
ggsave("output/liwc-posts-comments-over-time.png", width = 8, height = 8)
```







################################################################################
## LIWC Summary Dimensions
################################################################################

```{r}
liwc_posts_summary <-
  all_liwc %>%
  group_by(subreddit) %>%
  summarize(`Analytical Thinking` = median(`Analytical Thinking`),
            Clout = median(Clout),
            Authenticity = median(Authenticity),
            `Emotional Tone` = median(`Emotional Tone`)
            ) %>%
  pivot_longer(cols = `Analytical Thinking`:`Emotional Tone`,
               names_to = "liwc_measure",
               values_to = "liwc_score"
  ) %>%
  mutate(liwc_measure = 
           factor(liwc_measure, 
                  levels = c("Analytical Thinking", 
                             "Clout", 
                             "Authenticity", 
                             "Emotional Tone")
           ))
```

```{r}
ggplot(data = liwc_posts_summary) +
  geom_line(aes(x=liwc_measure, y=liwc_score, 
                group=subreddit, color=subreddit)
  ) +
  scale_color_manual(values = my_colors)  +
  xlab("LIWC measure") +
  ylab("LIWC score") +
  ggtitle("") +
  theme_bw() +
  theme(panel.grid.major = element_line(color = "gray90"),
        panel.grid.minor = element_blank(),
        axis.title=element_text(size=14, family='serif'),
        axis.text=element_text(size=10, family='serif'),
        legend.position='bottom',
        legend.box = 'vertical',
        legend.box.background = element_rect(),
        legend.title=element_text(size=14, family='serif'), 
        legend.text=element_text(size=10, family='serif')
  ) +
  guides(fill=guide_legend(nrow=3))
```

```{r, eval=FALSE}
ggsave("output/liwc-summary.png", width = 8, height = 4.5)
```

################################################################################
## LIWC Affect Dimensions
################################################################################

```{r}
liwc_posts_affect <-
  all_liwc %>%
  group_by(subreddit) %>%
  summarize(Positive_Emotions = median(posemo),
            Negative_Emotions = median(negemo),
            Anxiety = median(anx),
            Anger = median(anger),
            Sadness = median(sad)
            ) %>%
  pivot_longer(cols = Positive_Emotions:Sadness,
               names_to = "liwc_measure",
               values_to = "liwc_score"
  ) %>%
  mutate(liwc_measure = 
           factor(liwc_measure, 
                  levels = c("Positive_Emotions", "Negative_Emotions",
                             "Anxiety", "Anger", "Sadness")))
```

```{r}
ggplot(data = liwc_posts_affect) +
  geom_line(aes(x=factor(liwc_measure), y=liwc_score, 
                group=subreddit, color=subreddit)
  ) +
  scale_color_manual(values = my_colors)  +
  xlab("LIWC measure") +
  ylab("LIWC score") +
  ggtitle("") +
  theme_bw() +
  theme(panel.grid.major = element_line(color = "gray90"),
        panel.grid.minor = element_blank(),
        axis.title=element_text(size=14, family='serif'),
        axis.text=element_text(size=10, family='serif'),
        legend.position='bottom',
        legend.box = 'vertical',
        legend.box.background = element_rect(),
        legend.title=element_text(size=14, family='serif'), 
        legend.text=element_text(size=10, family='serif')
  ) +
  guides(fill=guide_legend(nrow=3))
```

```{r, eval=FALSE}
ggsave("output/liwc-affect.png", width = 8, height = 4.5)
```

################################################################################
## Cognitive Processes
################################################################################

```{r}
liwc_posts_cognitive <-
  all_liwc %>%
  group_by(subreddit) %>%
  summarize(Overall = median(cogproc),
            Insight = median(insight),
            Causal = median(cause),
            Discrepancies = median(discrep),
            Tentative = median(tentat),
            Certainty = median(certain),
            Differentiation = median(differ)
            ) %>%
  pivot_longer(cols = Overall:Differentiation,
               names_to = "liwc_measure",
               values_to = "liwc_score"
  ) %>%
  mutate(liwc_measure = 
           factor(liwc_measure, 
                  levels = c("Overall", "Insight", "Causal", "Discrepancies",
                             "Tentative", "Certainty", "Differentiation")))
```

```{r}
ggplot(data = liwc_posts_cognitive) +
  geom_line(aes(x=factor(liwc_measure), y=liwc_score, 
                group=subreddit, color=subreddit)
  ) +
  scale_color_manual(values = my_colors)  +
  xlab("LIWC measure") +
  ylab("LIWC score") +
  ggtitle("") +
  theme_bw() +
  theme(panel.grid.major = element_line(color = "gray90"),
        panel.grid.minor = element_blank(),
        axis.title=element_text(size=14, family='serif'),
        axis.text=element_text(size=10, family='serif'),
        legend.position='bottom',
        legend.box = 'vertical',
        legend.box.background = element_rect(),
        legend.title=element_text(size=14, family='serif'), 
        legend.text=element_text(size=10, family='serif')
  ) +
  guides(fill=guide_legend(nrow=3))
```

```{r, eval=FALSE}
ggsave("output/liwc-cognitive.png", width = 8, height = 4.5)
```

################################################################################
## Personal and Social Concerns
################################################################################

```{r}
liwc_posts_personal <-
  all_liwc %>%
  group_by(subreddit) %>%
  summarize(Work = median(work),
            Leisure = median(leisure),
            Home = median(home),
            Money = median(money),
            Social = median(social),
            Family = median(family),
            Friends = median(friend)
            ) %>%
  pivot_longer(cols = Work:Friends,
               names_to = "liwc_measure",
               values_to = "liwc_score"
  ) %>%
  mutate(liwc_measure = 
           factor(liwc_measure, 
                  levels = c("Work", "Leisure", "Home", "Money",
                             "Social", "Family", "Friends")))
```

```{r}
ggplot(data = liwc_posts_personal) +
  geom_line(aes(x=factor(liwc_measure), y=liwc_score, 
                group=subreddit, color=subreddit)
  ) +
  scale_color_manual(values = my_colors)  +
  xlab("LIWC measure") +
  ylab("LIWC score") +
  ggtitle("") +
  theme_bw() +
  theme(panel.grid.major = element_line(color = "gray90"),
        panel.grid.minor = element_blank(),
        axis.title=element_text(size=14, family='serif'),
        axis.text=element_text(size=10, family='serif'),
        legend.position='bottom',
        legend.box = 'vertical',
        legend.box.background = element_rect(),
        legend.title=element_text(size=14, family='serif'), 
        legend.text=element_text(size=10, family='serif')
  ) +
  guides(fill=guide_legend(nrow=3))
```

```{r, eval=FALSE}
ggsave("output/liwc-personal.png", width = 8, height = 4.5)
```

################################################################################
## Time Orientation
################################################################################

```{r}
liwc_posts_time <-
  all_liwc %>%
  group_by(subreddit) %>%
  summarize(Past_Focus = median(focuspast),
            Present_Focus = median(focuspresent),
            Future_Focus = median(focusfuture)
            ) %>%
  pivot_longer(cols = Past_Focus:Future_Focus,
               names_to = "liwc_measure",
               values_to = "liwc_score"
  ) %>%
  mutate(liwc_measure = 
           factor(liwc_measure, 
                  levels = c("Past_Focus", "Present_Focus", "Future_Focus")))
```

```{r}
ggplot(data = liwc_posts_time) +
  geom_line(aes(x=factor(liwc_measure), y=liwc_score, 
                group=subreddit, color=subreddit)
  ) +
  scale_color_manual(values = my_colors)  +
  xlab("LIWC measure") +
  ylab("LIWC score") +
  ggtitle("") +
  theme_bw() +
  theme(panel.grid.major = element_line(color = "gray90"),
        panel.grid.minor = element_blank(),
        axis.title=element_text(size=14, family='serif'),
        axis.text=element_text(size=10, family='serif'),
        legend.position='bottom',
        legend.box = 'vertical',
        legend.box.background = element_rect(),
        legend.title=element_text(size=14, family='serif'), 
        legend.text=element_text(size=10, family='serif')
  ) +
  guides(fill=guide_legend(nrow=3))
```

```{r, eval=FALSE}
ggsave("output/liwc-time.png", width = 8, height = 4.5)
```





################################################################################
## LIWC Measures in Focused Subreddits
################################################################################

```{r}
focus_subreddits <- 
  c("r/Teachers", "r/education",
    "r/Professors", "r/teaching",
    "r/academia", "r/highereducation")

subset_liwc <-
  all_liwc %>%
  filter(subreddit %in% focus_subreddits)
```

## LIWC Summary Dimensions

```{r}
liwc_posts_subset_summary <-
  subset_liwc %>%
  group_by(subreddit) %>%
  summarize(`Analytical Thinking` = median(`Analytical Thinking`),
            Clout = median(Clout),
            Authenticity = median(Authenticity),
            `Emotional Tone` = median(`Emotional Tone`)
            ) %>%
  pivot_longer(cols = `Analytical Thinking`:`Emotional Tone`,
               names_to = "liwc_measure",
               values_to = "liwc_score"
  ) %>%
  mutate(liwc_measure = 
           factor(liwc_measure, 
                  levels = c("Analytical Thinking", 
                             "Clout", 
                             "Authenticity", 
                             "Emotional Tone")
           ))
```

## LIWC Affect Dimensions

```{r}
liwc_posts_subset_affect <-
  subset_liwc %>%
  group_by(subreddit) %>%
  summarize(`Positive Emotions` = median(posemo),
            `Negative Emotions` = median(negemo),
            Anxiety = median(anx),
            Anger = median(anger),
            Sadness = median(sad)
            ) %>%
  pivot_longer(cols = `Positive Emotions`:Sadness,
               names_to = "liwc_measure",
               values_to = "liwc_score"
  ) %>%
  mutate(liwc_measure = 
           factor(liwc_measure, 
                  levels = c("Positive Emotions", "Negative Emotions",
                             "Anxiety", "Anger", "Sadness")),
         liwc_category = "Affect")
```

## Cognitive Processes

```{r}
liwc_posts_subset_cognitive <-
  subset_liwc %>%
  group_by(subreddit) %>%
  summarize(`Overall Cognitive` = median(cogproc),
            Insight = median(insight),
            Causal = median(cause),
            Discrepancies = median(discrep),
            Tentative = median(tentat),
            Certainty = median(certain),
            Differentiation = median(differ)
            ) %>%
  pivot_longer(cols = `Overall Cognitive`:Differentiation,
               names_to = "liwc_measure",
               values_to = "liwc_score"
  ) %>%
  mutate(liwc_measure = 
           factor(liwc_measure, 
                  levels = c("Overall Cognitive", "Insight", "Causal", "Discrepancies",
                             "Tentative", "Certainty", "Differentiation")),
         liwc_category = "Cognitive")
```

## Personal and Social Concerns

```{r}
liwc_posts_subset_personal <-
  subset_liwc %>%
  group_by(subreddit) %>%
  summarize(Work = median(work),
            Leisure = median(leisure),
            Home = median(home),
            Money = median(money),
            Social = median(social),
            Family = median(family),
            Friends = median(friend)
            ) %>%
  pivot_longer(cols = Work:Friends,
               names_to = "liwc_measure",
               values_to = "liwc_score"
  ) %>%
  mutate(liwc_measure = 
           factor(liwc_measure, 
                  levels = c("Work", "Leisure", "Home", "Money",
                             "Social", "Family", "Friends")),
         liwc_category = "Personal")
```

## Combine All LIWC Measures

```{r}
liwc_subset <-
  liwc_posts_subset_summary %>%
  bind_rows(liwc_posts_subset_affect) %>%
  bind_rows(liwc_posts_subset_cognitive) %>%
  bind_rows(liwc_posts_subset_personal) %>%
  rename(Subreddit = subreddit)
```

## Visualize Subset of Subreddits

```{r}
my_colors6 <- my_colors[c(2, 4, 5, 6, 7, 8)]
```

```{r}
ggplot(data = liwc_subset) +
  geom_line(alpha = 0.5,
            linewidth = 1.5,
            aes(x=factor(liwc_measure), y=liwc_score, 
                group=Subreddit, color=Subreddit)
  ) +
  scale_color_manual(values = my_colors6) +
  xlab("LIWC measure") +
  ylab("LIWC score") +
  ggtitle("") +
  theme_bw() +
  theme(panel.grid.major = element_line(color = "gray90"),
        panel.grid.minor = element_blank(),
        axis.title = element_text(size=18, family='serif'),
        axis.text = element_text(size=14, family='serif'),
        legend.position = 'bottom',
        legend.box = 'vertical',
        legend.box.background = element_rect(),
        legend.title=element_text(size=18, family='serif'), 
        legend.text=element_text(size=14, family='serif'),
        axis.text.x = element_text(angle=60, hjust=1)
  ) +
  guides(color = guide_legend(override.aes = list(alpha = 0.8, linewidth = 2.0)))
```

```{r, eval=FALSE}
ggsave("output/liwc-subset.png", width = 8, height = 6)
```



```{r}
liwc_subset2 <-
  liwc_subset %>%
  filter(liwc_measure %in% c("Analytical Thinking", "Clout",
                             "Authenticity", "Emotional Tone"))
```

```{r}
ggplot(data = liwc_subset2) +
  geom_line(alpha = 0.8,
            linewidth = 1.5,
            aes(x=factor(liwc_measure), y=liwc_score, 
                group=Subreddit, color=Subreddit)
  ) +
  scale_color_manual(values = my_colors6) +
  xlab("LIWC measure") +
  ylab("LIWC score") +
  ggtitle("") +
  theme_bw() +
  theme(panel.grid.major = element_line(color = "gray90"),
        panel.grid.minor = element_blank(),
        axis.title = element_text(size=18, family='serif'),
        axis.text = element_text(size=14, family='serif'),
        legend.position = 'bottom',
        legend.box = 'vertical',
        legend.box.background = element_rect(),
        legend.title=element_text(size=18, family='serif'), 
        legend.text=element_text(size=14, family='serif')
  ) +
  guides(color = guide_legend(override.aes = list(alpha = 0.8, linewidth = 2.0)))
```

```{r, eval=FALSE}
ggsave("output/liwc-subset-zoom.png", width = 8, height = 6)
```

```{r}
ggplot(data = liwc_subset2,
       aes(x=factor(liwc_measure), y=liwc_score, 
                group=Subreddit, color=Subreddit)
) +
  geom_point(size = 5) + 
  geom_segment(linewidth = 2,
                aes(x=factor(liwc_measure), 
                    xend = factor(liwc_measure), 
                    y = 0, 
                    yend = liwc_score)) +
  coord_flip() +
  facet_wrap(vars(Subreddit), nrow = 2) +
  scale_color_manual(values = my_colors6) +
  xlab("LIWC measure") +
  scale_x_discrete(limits=rev) +
  ylab("LIWC score") +
  ylim(0, 80) +
  theme_bw() +
  theme(panel.grid.major = element_line(color = "gray90"),
        panel.grid.minor = element_blank(),
        axis.title = element_text(size=18, family='serif'),
        axis.text = element_text(size=14, family='serif'),
        strip.text = element_text(size=14, family='serif'),
        legend.position = 'none'
  )
```

```{r, eval=FALSE}
ggsave("output/liwc-subset-lollipop.png", width = 8, height = 6)
```



```{r}
liwc_subset3 <-
  liwc_subset %>%
  filter(liwc_measure %in% c("Social", "Overall Cognitive", "Work", "Positive Emotions", "Negative Emotions"))
```

```{r}
ggplot(data = liwc_subset3) +
  geom_line(alpha = 0.8,
            linewidth = 1.5,
            aes(x=factor(liwc_measure), y=liwc_score, 
                group=Subreddit, color=Subreddit)
  ) +
  scale_color_manual(values = my_colors6) +
  xlab("LIWC measure") +
  ylab("LIWC score") +
  ggtitle("") +
  theme_bw() +
  theme(panel.grid.major = element_line(color = "gray90"),
        panel.grid.minor = element_blank(),
        axis.title = element_text(size=18, family='serif'),
        axis.text = element_text(size=14, family='serif'),
        legend.position = 'bottom',
        legend.box = 'vertical',
        legend.box.background = element_rect(),
        legend.title=element_text(size=18, family='serif'), 
        legend.text=element_text(size=14, family='serif')
  )
```





################################################################################
## Text Analysis
################################################################################

```{r, echo=FALSE, warning=FALSE, message=FALSE, eval=FALSE}
#spacyr::spacy_install()
spacy_initialize(model = "en_core_web_sm")
```

```{r, echo=FALSE}
singularize <- function (x) {
  spacyr::spacy_parse(x, tag = TRUE) %>%
    mutate(token = ifelse(tag == "NNS",
                          lemma,
                          token)
           ) %>%
    group_by(doc_id) %>% 
    summarize(text = paste0(token, collapse = " ")) %>%
    quanteda::corpus()
}
```

```{r, echo=FALSE} 
my_extra_stopwords <-
  c("NA", "just", "also", "can", "like", "etc", "lot", "many", "much", 
    "even", "sure", "n't", "re", "ve"
  )

my_compound_words <-
  c("computer science", "information science", "data science",
    "information technology", "information system", 
    "computer engineering", "computer system", "computer network",
    "computer security", "information security", "network security",
    "natural language processing", "artificial intelligence", "machine learning",
    "large language model", "generative ai",
    "data analytics", "learning analytics", "big data"
)
```

```{r}
subreddit_post_text_all <-
  posts_liwc %>%
  filter(subreddit != "r/Professors") %>%
  mutate(post_text = paste(title, post_text)) %>%
  group_by(subreddit) %>%
  summarize(all_post_text = paste(post_text, collapse = " "))
subreddit_comments_text_all <- 
  comments_liwc %>%
  filter(subreddit != "r/Professors") %>%
  group_by(subreddit) %>%
  summarize(all_comment_text = paste(comment_text, collapse = " "))
subreddit_text_all <-
  subreddit_post_text_all %>%
  full_join(subreddit_comments_text_all, by = "subreddit") %>%
  group_by(subreddit) %>%
  summarize(all_text = paste(all_post_text, all_comment_text, collapse = " "))
```

```{r}
subreddit_post_text_prof <-
  posts_liwc %>%
  filter(subreddit == "r/Professors") %>%
  mutate(post_text = paste(title, post_text)) %>%
  group_by(subreddit) %>%
  summarize(all_text = paste(post_text, collapse = " "))
subreddit_comment_text_prof1 <- 
  comments_liwc %>%
  filter(subreddit == "r/Professors") %>%
  head(2000) %>%
  group_by(subreddit) %>%
  summarize(all_text = paste(comment_text, collapse = " "))
subreddit_comment_text_prof2 <- 
  comments_liwc %>%
  filter(subreddit == "r/Professors") %>%
  tail(1829) %>%
  group_by(subreddit) %>%
  summarize(all_text = paste(comment_text, collapse = " "))
```

```{r, echo=FALSE}
dfm_terms_all <-
  subreddit_text_all %>%
  quanteda::corpus(text_field = 'all_text',
                   docid_field = 'subreddit') %>%
  singularize() %>%
  quanteda::tokens(
    remove_separators = TRUE,
    remove_punct = TRUE,
    remove_symbols = TRUE,
    remove_numbers = TRUE,
    remove_url = TRUE) %>%
  quanteda::tokens_select(min_nchar = 2L) %>% #Filter: at least 2 letters 
  quanteda::tokens_compound(phrase(my_compound_words)) %>%
  quanteda::dfm(tolower = TRUE) %>%
  quanteda::dfm_remove(c(my_extra_stopwords,
                 quanteda::stopwords("english")))

dfm_terms_all$subreddit <- unique(subreddit_text_all$subreddit)

quanteda::ndoc(dfm_terms_all); quanteda::nfeat(dfm_terms_all)
```

```{r, echo=FALSE}
dfm_terms_post_prof <-
  subreddit_post_text_prof %>%
  quanteda::corpus(text_field = 'all_text',
                   docid_field = 'subreddit') %>%
  singularize() %>%
  quanteda::tokens(
    remove_separators = TRUE,
    remove_punct = TRUE,
    remove_symbols = TRUE,
    remove_numbers = TRUE,
    remove_url = TRUE) %>%
  quanteda::tokens_select(min_nchar = 2L) %>% #Filter: at least 2 letters 
  quanteda::tokens_compound(phrase(my_compound_words)) %>%
  quanteda::dfm(tolower = TRUE) %>%
  quanteda::dfm_remove(c(my_extra_stopwords,
                 quanteda::stopwords("english")))

dfm_terms_post_prof$subreddit <- "r/Professors"

quanteda::ndoc(dfm_terms_post_prof); quanteda::nfeat(dfm_terms_post_prof)
```

```{r, echo=FALSE}
dfm_terms_comment_prof1 <-
  subreddit_comment_text_prof1 %>%
  quanteda::corpus(text_field = 'all_text',
                   docid_field = 'subreddit') %>%
  singularize() %>%
  quanteda::tokens(
    remove_separators = TRUE,
    remove_punct = TRUE,
    remove_symbols = TRUE,
    remove_numbers = TRUE,
    remove_url = TRUE) %>%
  quanteda::tokens_select(min_nchar = 2L) %>% #Filter: at least 2 letters 
  quanteda::tokens_compound(phrase(my_compound_words)) %>%
  quanteda::dfm(tolower = TRUE) %>%
  quanteda::dfm_remove(c(my_extra_stopwords,
                 quanteda::stopwords("english")))

dfm_terms_comment_prof1$subreddit <- "r/Professors"

quanteda::ndoc(dfm_terms_comment_prof1); quanteda::nfeat(dfm_terms_comment_prof1)
```

```{r, echo=FALSE}
dfm_terms_comment_prof2 <-
  subreddit_comment_text_prof2 %>%
  quanteda::corpus(text_field = 'all_text',
                   docid_field = 'subreddit') %>%
  singularize() %>%
  quanteda::tokens(
    remove_separators = TRUE,
    remove_punct = TRUE,
    remove_symbols = TRUE,
    remove_numbers = TRUE,
    remove_url = TRUE) %>%
  quanteda::tokens_select(min_nchar = 2L) %>% #Filter: at least 2 letters 
  quanteda::tokens_compound(phrase(my_compound_words)) %>%
  quanteda::dfm(tolower = TRUE) %>%
  quanteda::dfm_remove(c(my_extra_stopwords,
                 quanteda::stopwords("english")))

dfm_terms_comment_prof2$subreddit <- "r/Professors"

quanteda::ndoc(dfm_terms_comment_prof2); quanteda::nfeat(dfm_terms_comment_prof2)
```

```{r, echo=FALSE, message=FALSE}
term_freq_all <- 
  quanteda.textstats::textstat_frequency(dfm_terms_all, n = 100000, groups = subreddit) %>%
  rename(term = feature, subreddit = group)
term_freq_30 <- 
  quanteda.textstats::textstat_frequency(dfm_terms_all, n = 30, groups = subreddit) %>%
  rename(term = feature, subreddit = group)

term_freq_post_prof_all <- 
  quanteda.textstats::textstat_frequency(dfm_terms_post_prof, n = 100000, groups = subreddit) %>%
  rename(term = feature, subreddit = group)
term_freq_post_prof_30 <- 
  quanteda.textstats::textstat_frequency(dfm_terms_post_prof, n = 30, groups = subreddit) %>%
  rename(term = feature, subreddit = group)

term_freq_comment_prof1_all <- 
  quanteda.textstats::textstat_frequency(dfm_terms_comment_prof1, n = 100000, groups = subreddit) %>%
  rename(term = feature, subreddit = group)
term_freq_comment_prof1_30 <- 
  quanteda.textstats::textstat_frequency(dfm_terms_comment_prof1, n = 30, groups = subreddit) %>%
  rename(term = feature, subreddit = group)

term_freq_comment_prof2_all <- 
  quanteda.textstats::textstat_frequency(dfm_terms_comment_prof2, n = 100000, groups = subreddit) %>%
  rename(term = feature, subreddit = group)
term_freq_comment_prof2_30 <- 
  quanteda.textstats::textstat_frequency(dfm_terms_comment_prof2, n = 30, groups = subreddit) %>%
  rename(term = feature, subreddit = group)

term_freq_df <- 
  term_freq_all %>%
  bind_rows(term_freq_post_prof_all) %>%
  bind_rows(term_freq_comment_prof1_all) %>%
  bind_rows(term_freq_comment_prof2_all) %>%
  group_by(subreddit, term) %>%
  summarize(frequency = sum(frequency)) %>%
  ungroup()

term_freq_df_30 <- 
  term_freq_30 %>%
  bind_rows(term_freq_post_prof_30) %>%
  bind_rows(term_freq_comment_prof1_30) %>%
  bind_rows(term_freq_comment_prof2_30) %>%
  group_by(subreddit, term) %>%
  summarize(frequency = sum(frequency)) %>%
  ungroup()
```

```{r, echo=FALSE} 
term_freq_subset_all <-
  term_freq_df %>%
  filter(subreddit %in% focus_subreddits)
length(unique(term_freq_subset_all$term))

term_freq_subset_30 <-
  term_freq_df_30 %>%
  filter(subreddit %in% focus_subreddits)
length(unique(term_freq_subset_30$term))

top30terms <- unique(term_freq_subset_30$term)

term_freq <-
  term_freq_subset_all %>%
  filter(term %in% top30terms) %>%
  tidyr::complete(term, subreddit, fill = list(frequency = 0)) %>%
  group_by(subreddit) %>%
  mutate(frequency = as.numeric(scale(frequency, center = FALSE))) %>%
  ungroup() %>%
  select(term, subreddit, frequency)
```

```{r, echo=FALSE} 
term_freq_seriated <-
  term_freq %>%
  pivot_wider(names_from = term, values_from = frequency) %>%
  column_to_rownames("subreddit") %>%
  seriation::seriate(method = "PCA")
  
terms_ordered <-
  unique(term_freq$term)[seriation::get_order(term_freq_seriated, dim=2)]
subreddits_ordered <-
  unique(term_freq$subreddit)[seriation::get_order(term_freq_seriated, dim=1)]

term_freq_to_plot <-
  term_freq %>%
  mutate(
    term = factor(term, levels = rev(terms_ordered)),
    subreddit = factor(subreddit, levels = subreddits_ordered)
  )
```

```{r, echo=FALSE} 
ggplot(term_freq_to_plot, aes(x = subreddit, y = term)) +
  geom_tile(aes(fill = frequency), color = 'white') + 
  scale_fill_viridis(name = "Frequency:",
                     rescaler = 
                       function(x, to = c(0, 1), from = NULL) {
                         ifelse(x < 2.5, 
                                scales::rescale(x,
                                                to = to,
                                                from = c(min(x, na.rm = TRUE), 2.5)), 
                                1)
                       }) +
  theme_bw() +
  scale_x_discrete(position = "top") +
  xlab(NULL) +
  theme(legend.title = element_text(size = 12, face = "bold"),
        axis.text.x = element_text(face = "bold"),
        legend.text = element_text(size = 10),
        legend.box.background = element_rect(colour = "black")
  )
```

```{r, eval=FALSE}
ggsave("output/term-frequency-plot.png", width = 8, height = 8)
```