1.1 Change Variable Types

convert $is_retweet into a boolean

data_raw$is_retweet <- as.logical(data_raw$is_retweet)

convert $*_tweetid vars into strings

data_raw <- data_raw %>% 
  mutate_at(vars(ends_with("tweetid")),
            funs(as.character))

Now we can skim just the $*_tweetid vars and $is_retweet

data_raw %>% 
  select(is_retweet, ends_with("tweetid")) %>% 
  skimr::skim_to_wide(noten_raw) %>%
  knitr::kable("html")

Ok, now we know that 3.333.184 (~1/3) Tweets are Retweets (of 1.725.841 unique Tweets). Good to know for any Natural Language Processing Method which depends on statistics, i.e. Topic Modelling, or when building a corpus for descriptive analyses.

1.2 Remove Duplicates

It’s probably more efficient to remove duplicates as the first step. This reduces the data object we’re working with.

data_unique <- data_raw %>% filter(is_retweet == FALSE)

Now we’re down to 5707611 unique Tweets in 57 Tweet languages by 3548 Users with 11 account languages.

1.3 Reduce/Recode Language Variables

$tweet_language

data_unique %>% 
  group_by(tweet_language) %>% 
  count() %>% 
  filter(n > 1000) %>% 
  arrange(desc(n)) %>%
  knitr::kable(format = "html")

That’s quite a lot, even if we only consider Tweet languages with n > 1000 Tweets.

merge NA and und[defined]

data_unique <- data_unique %>% 
  mutate(tweet_language = if_else(is.na(tweet_language), "und", tweet_language))

recode all langs with n < 5000 as “other”

We need to reduce the scope of Tweet languages for now. 5000 is only slightly less than 1% of the unique Tweets, so this sounds like a good threshold.

# I'm very sure that there's a more elegant solution for mutating observations row-wise based on grouped counts... However, whatever works, works.

other_langs <- data_unique %>% 
  group_by(tweet_language) %>% 
  count() %>% 
  filter(n < 5000) %>% 
  select(tweet_language)

data_unique <- data_unique %>% 
  mutate(tweet_language = 
           if_else(tweet_language %in% other_langs$tweet_language, "other_44",
                                  tweet_language))

n_distinct(data_unique$tweet_language)

## [1] 12

We’re down to 13 language categories for the Tweets. That’s far better!

$account_language

data_unique %>% 
  group_by(account_language) %>% 
  distinct(userid) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  knitr::kable("html")

So there’s 11 account languages. I’m going to focus on languages with n > 100 accounts, and recode the rest as “other_6” (since we can assume “uk” == “en-gb”, that’s one language less).

other_langs_acc <- data_unique %>% 
  distinct(userid, .keep_all = TRUE) %>% 
  group_by(account_language) %>% 
  count() %>% 
  filter(n < 100) %>% 
  select(account_language)

data_unique <- data_unique %>% 
  mutate(account_language = 
           if_else(account_language %in% other_langs_acc$account_language, "other_6",
                                  account_language))
n_distinct(data_unique$account_language)

## [1] 5

Great! Now we’re down to 5 account languages.

1.4 IRA Dataset Languages: Summary

Let’s see how many different languages we have by now.

unique(c(data_unique$tweet_language, data_unique$account_language))

##  [1] "ru"       "bg"       "en"       "und"      "other_44" "de"      
##  [7] "uk"       "tl"       "es"       "ar"       "fr"       "in"      
## [13] "sr"       "other_6"

Now let’s create a preliminary overview.

data_count_by_tweet_lang <- data_unique %>%
  # filter(is_retweet == TRUE) %>% 
  group_by(tweet_language) %>%
  distinct(tweetid) %>% 
  count() %>%
  rename(Tweets = n)

data_count_by_account_lang <- data_unique %>%
  # filter(is_retweet == TRUE) %>% 
  group_by(account_language) %>%
  distinct(userid) %>% 
  count() %>%
  rename(Accounts = n)

lang_stats <- full_join(data_count_by_tweet_lang, data_count_by_account_lang,
  by = c("tweet_language" = "account_language")) %>%
  rename(Language = tweet_language) %>%
  mutate(
    T.Share = round(Tweets / sum(.$Tweets, na.rm = TRUE) * 100, 2),
    A.Share = round(Accounts / sum(.$Accounts, na.rm = TRUE) * 100, 2)
  ) %>%
  select(Language, Tweets, T.Share, Accounts, A.Share) %>%
  arrange(desc(Accounts))

lang_stats %>%
  knitr::kable("html",
    format.args = list(
      big.mark = ".",
      decimal.mark = ","),
    caption = "#TwitterDump 2018 – Russian InfoOP Dataset: Languages (unique Tweets)"
  )

That’s already interesting, but let’s not jump to conclusion about who tweeted in what language, yet… This summary alone does enable us to claim that, for instance, Russian accounts where responsible for all the Russian Tweets, and so on.

What’s really striking is that 50% of the Tweets from this 9M Tweets IRA dataset are in Russian (or at least labelled as such), which does not quite fit the dominant narrative of a solely US-centric information operation. These numbers show that Russia’s activities were concered with Russian-speaking people as much as with an English-speaking audience (among German and Spanish).

Check for any remaining language NA’s

data_unique %>% filter(is.na(tweet_language) | is.na(account_language)) %>% 
  count()

## # A tibble: 1 x 1
##       n
##   <int>
## 1     0

That’s great news so far!

(Optional: recode if(tweet_lang == NA &/| user_lang == NA))

Since we’ve reduced our dataset and already recoded the NAs, this step is not necessary anymore (before that, I worked without is_retweet == FALSE and things looked a bit different). However, I’m just leaving the syntax here, since it might be useful to others (and to myself).

data_recoded <- data_unique %>%
  mutate(tweet_language = if_else(is.na(tweet_language) & is.na(account_language),
                                  "und",
                            if_else(is.na(tweet_language) & !is.na(account_language),
                                    account_language,
                          tweet_language)
                          )
         ) %>% 
  mutate(account_language = if_else(is.na(tweet_language) & is.na(account_language),
                                  "und",
                              if_else(is.na(account_language) & !is.na(tweet_language),
                                      tweet_language,
                            account_language)
                            )
         )

This is a good moment in our data preparation cycle to create a hardcopy of our processed data_unique object in a .rds file. This way, we won’t have to redo all the wrangling and recoding we did so far, and can just start with any in-depth analysis by loading the object with data_unique <- readRDS(file). ANd we can reduce our local in-memory load by rm(data_raw)

# data_path <- here::here("data", "IRA_Tweets", "/")
# if (!dir.exists(data_path)) dir.create(data_path)
saveRDS(data_unique, str_c(data_path, "infoops_data_processed.rds"))
rm(data_raw)
data_unique <- readRDS(str_c(data_path, "infoops_data_processed.rds"))

2.1 Create Language-specific Subsets

This is also a good moment to create language-specific or other intereting subsets from our refined dataset.

German subset

german_subset <- data_unique %>% filter(tweet_language == "de" | account_language == "de")

The German subset has 98064 Tweets by 782 users.

Undefined Subset

undefined_subset <- data_unique %>% 
  filter(tweet_language == "und" | account_language == "und" | 
           is.na(tweet_language) | is.na(account_language))

The undefined (“und” & “NA”) subset has 404557 Tweets by 2867 users.

2.2 Summary Plots: Languages & General Activity

data_unique %>%
  group_by(tweet_language) %>%
  summarise(n = n()) %>%
  mutate(
    share = n / sum(n),
    tweet_language = case_when(
      tweet_language == "" ~ "unspec.",
      tweet_language == "und" ~ "undef.",
      TRUE ~ tweet_language
    )
  ) %>%
  arrange(desc(n)) %>%
  ggplot(aes(area = share)) +
  treemapify::geom_treemap(aes(fill = n), alpha = 0.8) +
  treemapify::geom_treemap_text(
    aes(label = paste0(tweet_language, "\n(", round(share * 100, 1), "%)"))
  ) +
  scale_fill_viridis_c(direction = -1, option = "D") +
  labs(
    title = "#TwitterDump 2018 – Russian InfoOP Dataset: Shares of Languages (Tweets)",
    subtitle = str_c("Total of ", 
      n_distinct(data_unique$tweetid), " unique Tweets (no RTs) from ",
      n_distinct(data_unique$userid), " unique Users"
    ),
    caption = str_c("@fubits")
  ) +
  guides(fill = FALSE)

data_unique %>%
  group_by(account_language) %>%
  summarise(n = n()) %>%
  mutate(
    share = n / sum(n),
    account_language = case_when(
      account_language == "" ~ "unspec.",
      account_language == "und" ~ "undef.",
      TRUE ~ account_language
    )
  ) %>%
  arrange(desc(n)) %>%
  ggplot(aes(area = share)) +
  treemapify::geom_treemap(aes(fill = n), alpha = 0.8) +
  treemapify::geom_treemap_text(
    aes(label = paste0(account_language, "\n(", round(share * 100, 1), "%)"))
  ) +
  scale_fill_viridis_c(direction = -1, option = "D") +
  labs(
    title = "#TwitterDump 2018 – Russian InfoOP Dataset: Shares of Languages (Accounts)",
    subtitle = str_c("Total of ", 
      n_distinct(data_unique$tweetid), " unique Tweets (no RTs) from ",
      n_distinct(data_unique$userid), " unique Users"
    ),
    caption = str_c("@fubits")
  ) +
  guides(fill = FALSE)

data_counts <- data_unique %>%
  group_by(userid) %>%
  mutate(Account_Lang = account_language) %>% 
  summarise(
    Created = unique(account_creation_date),
    Account_Lang = unique(Account_Lang),
    Tweets = n(),
    RT = sum(retweet_count),
    Follower = unique(follower_count),
    Following = unique(following_count),
    Influence = (((Follower + 1) / (Following + 1)) + (Follower + 1)),
    Tweet_Langs = list(tweet_language),
    Tweet_Langs_Counts = list(unlist(Tweet_Langs) %>% fct_count())
  ) %>%
  arrange(desc(Tweets))

data_counts[1,]$Tweet_Langs_Counts[[1]] %>% knitr::kable("html")

data_counts %>%  
  select(-userid, -Tweet_Langs) %>% 
  head(10) %>% knitr::kable("html", digits = 0)

ggplot(data_counts, aes(x = Follower, y = Following)) +
  geom_point(aes(size = Tweets, color = userid, alpha = Influence)) +
  scale_color_viridis_d(direction = -1) +
  scale_alpha_continuous(range = c(0.3,1),
                         breaks = scales::pretty_breaks(5)) +
  scale_size(range = c(1,5), labels = scales::number_format(big.mark = ".",
                                                    decimal.mark = ",")) +
  scale_x_continuous(breaks = scales::pretty_breaks(6),
                     labels = scales::number_format(big.mark = ".",
                                                    decimal.mark = ",")) +
  scale_y_continuous(breaks = scales::pretty_breaks(6),
                     labels = scales::number_format(big.mark = ".",
                                                    decimal.mark = ",")) +
  coord_fixed() +
  theme_minimal() +
  labs(
    title = "#TwitterDump 2018 – Russian InfoOP Dataset: Account Stats",
    subtitle = str_c("Total of ", 
      n_distinct(data_unique$tweetid), " unique Tweets (no RTs) from ",
      n_distinct(data_unique$userid), " unique Users"
    ),
    caption = str_c("@fubits"),
    #x = "",
    # y = "",
    size = "# of Tweets",
    alpha = "Alpha: # Retweets"
  ) +
    guides(color = FALSE, alpha = FALSE)

ggplot(data_counts, aes(x = Follower, y = Following)) +
  geom_point(aes(size = Tweets, color = fct_infreq(Account_Lang), alpha = Influence)) +
  scale_color_viridis_d(option = "B", direction = 1) +
  scale_alpha_continuous(range = c(0.3,1),
                         labels = scales::number_format(big.mark = ".",
                                                    decimal.mark = ",")) +
  scale_size(range = c(1,5), labels = scales::number_format(big.mark = ".",
                                                    decimal.mark = ",")) +
  scale_x_continuous(breaks = scales::pretty_breaks(6),
                     labels = scales::number_format(big.mark = ".",
                                                    decimal.mark = ",")) +
  scale_y_continuous(breaks = scales::pretty_breaks(6),
                     labels = scales::number_format(big.mark = ".",
                                                    decimal.mark = ",")) +
  coord_fixed() +
  theme_minimal() +
  labs(
    title = "#TwitterDump 2018 – Russian InfoOP Dataset: Activity by Account Language",
    subtitle = str_c("Total of ", 
      n_distinct(data_unique$tweetid), " unique Tweets (no RTs) from ",
      n_distinct(data_unique$userid), " unique Users"
    ),
    caption = str_c("@fubits"),
    #x = "",
    # y = "",
    size = "# of Tweets",
    color = "Account Language"
  ) +
    guides(alpha = FALSE,
           colour = guide_legend(override.aes = list(size = 5, stroke = 1.5))
           )

influencers <- data_counts %>% 
  arrange(desc(Influence)) %>% 
  top_n(15, Influence)

data_counts %>% 
  arrange(desc(Influence)) %>% 
  ggplot(data = ., aes(x = Follower, y = Following)) +
    geom_point(aes(size = Tweets, color = fct_infreq(Account_Lang), alpha = Influence)) +
    ggrepel::geom_label_repel(data = influencers,
                            aes(label = lubridate::year(Created),
                                fill = Account_Lang),
                            alpha = 0.7) +
    scale_color_viridis_d(option = "B", direction = 1) +
    scale_alpha_continuous(range = c(0.3,1),
                           labels = scales::number_format(big.mark = ".",
                                                      decimal.mark = ",")) +
    scale_size(range = c(1,5), labels = scales::number_format(big.mark = ".",
                                                      decimal.mark = ",")) +
    scale_x_continuous(breaks = scales::pretty_breaks(6),
                       labels = scales::number_format(big.mark = ".",
                                                      decimal.mark = ",")) +
    scale_y_continuous(breaks = scales::pretty_breaks(6),
                       labels = scales::number_format(big.mark = ".",
                                                      decimal.mark = ",")) +
    coord_fixed() +
    theme_minimal() +
    labs(
      title = "#TwitterDump 2018 – Russian InfoOP Dataset: Top 15 (Influence) - Account Creation Date",
      subtitle = str_c("Total of ", 
        n_distinct(data_unique$tweetid), " unique Tweets (no RTs) from ",
        n_distinct(data_unique$userid), " unique Users"
      ),
      caption = str_c("@fubits"),
      #x = "",
      # y = "",
      size = "# of Tweets",
      color = "Account Language"
    ) +
      guides(alpha = FALSE,
             colour = FALSE)

data_counts %>% 
  arrange(desc(Influence)) %>% 
  select(userid, Account_Lang, Follower, Following) %>% 
  head(15) %>% knitr::kable("html")

data_raw %>% filter(userid == 2518710111) %>% select(userid, user_display_name, user_screen_name, account_language) %>% head(1) %>% knitr::kable("html")

data_counts %>% 
  ggplot(data = ., aes(x = Follower, y = Following)) +
    geom_point(aes(size = Tweets, color = fct_infreq(Account_Lang), alpha = RT)) +
    ggrepel::geom_label_repel(data = data_counts[1:15,],
                            aes(label = lubridate::year(Created),
                                fill = Account_Lang),
                            alpha = 0.7) +
    scale_color_viridis_d(option = "B", direction = 1) +
    scale_alpha_continuous(range = c(0.3,1),
                           labels = scales::number_format(big.mark = ".",
                                                      decimal.mark = ",")) +
    scale_size(range = c(1,5), labels = scales::number_format(big.mark = ".",
                                                      decimal.mark = ",")) +
    scale_x_continuous(breaks = scales::pretty_breaks(6),
                       labels = scales::number_format(big.mark = ".",
                                                      decimal.mark = ",")) +
    scale_y_continuous(breaks = scales::pretty_breaks(6),
                       labels = scales::number_format(big.mark = ".",
                                                      decimal.mark = ",")) +
    coord_fixed() +
    theme_minimal() +
    labs(
      title = "#TwitterDump 2018 – Russian InfoOP Dataset: Top 15 (# Tweets) - Account Creation Date",
      subtitle = str_c("Total of ", 
        n_distinct(data_unique$tweetid), " unique Tweets (no RTs) from ",
        n_distinct(data_unique$userid), " unique Users"
      ),
      caption = str_c("@fubits"),
      #x = "",
      # y = "",
      size = "# of Tweets",
      color = "Account Language"
    ) +
      guides(alpha = FALSE,
             colour = FALSE)

2.3 Languages: Accounts vs Tweets

Now it’s time to have look at the account language to tweet languages relations.

This is what the Top 20 (of 57) language combinations look like:

data_unique %>% 
  group_by(account_language, tweet_language) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  head(20) %>% 
  knitr::kable("html", caption = "Top 20 Language Combinations from the IRA Dataset")

Ok, so we probably could have expected that Russian and English speaking accounts would mostly tweet in their respective languages. But who would have suspected that 185.803 Russian language tweets were posted by English-speaking accounts? Right :)

Now let’s visualize all the 57 Account Language -> Tweet Language combinations. For an easier understanding of these plots, just keep in mind that Tweets are posted by accounts, so the Tweet language (bottom) is our dependent variable here.

data_unique %>% 
  group_by(account_language, tweet_language) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
    ggplot() +
    geom_tile(aes(x = tweet_language,
                  y = account_language,
                  fill = n)) +
    scale_fill_viridis_c(option = "B", direction = -1,
                         breaks = scales::pretty_breaks(4),
                         labels = scales::number_format(big.mark = ".",
                                                        decimal.mark = ",")) + 
    coord_fixed() +
    theme_minimal() +
    labs(
      title = "#TwitterDump 2018 – Russian InfoOP Dataset: Language Matrix",
      subtitle = str_c(
        "Subset of ", n_distinct(data_unique$tweetid),
        " unique Tweets (no RTs) from ",
        n_distinct(data_unique$userid), " unique Users"
      ),
      caption = str_c("@fubits"),
      fill = "Language Combo:\nTotals",
      x = "Tweet Language",
      y = "Account Language"
    )

data_unique %>% 
  group_by(account_language, tweet_language) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
    ggplot() +
    geom_tile(aes(x = tweet_language, 
                  y = account_language,
                  fill = n/sum(n))) +
    scale_fill_viridis_c(option = "B", direction = -1,
                         breaks = scales::pretty_breaks(6),
                         labels = scales::percent_format(accuracy = 1)) + 
    coord_fixed() +
    theme_minimal() +
  # theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
    labs(
      title = "#TwitterDump 2018 – Russian InfoOP Dataset: Language Matrix",
      subtitle = str_c(
        "Subset of ", n_distinct(data_unique$tweetid), 
        " unique Tweets (no RTs) from ",
        n_distinct(data_unique$userid), " unique Users"
      ),
      caption = str_c("@fubits"),
      fill = "Language Combo:\nShare of Total"
    )

Alright, that’s enough exploration and heavy data wrangling for today. Stay tuned for Part 2: Content Analysis

Here’s just a teaser of what is expecting us:

data_unique %>%
  filter(tweet_language == "ru") %>% 
  select(tweet_text) %>% 
  head(10) %>%
  knitr::kable("html")

Or what about “undefined”

data_unique %>%
  filter(tweet_language == "und") %>% 
  select(tweet_text) %>% 
  head(10) %>% 
  knitr::kable("html")

Btw, have you already discovered the US-Republican-sponsored (sic!) RussiaTweets.com Project?