Thoughts

A previous post here detailed a simple function (from the uspols package) for extracting the timeline of the Trump presidency from Wikipedia. In this post, then, we turn this timeline into a Twitter bot – one that remembers daily what happened four years ago in the Trump presidency.

A fairly trivial task; what is a bit tricky, however, is (1) dealing with Twitter’s 280 character limit, and (2) automating the posting of Twitter threads, as Wikipedia’s daily accounting of the last four years can be fairly detailed. Via the rtweet package. For good measure, we demonstrate an unsupervised approach to adding hashtags to our tweet-threads in the form of named entities via spacy / spacyr.

Timeline content

The Trump timeline can be extracted using the uspols::uspols_wiki_timeline() function. A static table will shortly suffice.

# devtools::install_github("jaytimm/uspols")
library(tidyverse)
locals1 <- uspols::uspols_wiki_timeline()
## feature-izing Events --
locals1$nsent <- tokenizers::count_sentences(locals1$Events)
locals1$nchar1 <- nchar(locals1$Events)
## -- 
locals1 <- subset(locals1, nchar1 > 0)

Details of table content returned from uspols_wiki_timeline(): we consider the 699th day of the Trump presidency. A Thursday.

eg <- locals1 %>%filter(daypres == 699)
eg %>% select(quarter:dow) %>% slice(1) %>% knitr::kable()

As detailed below, Wikipedia summarizes the day’s happenings as individual “Events.” Some days were more eventful than others during the past four years. I have added a bullet column for simple enumeration; day 699, then, included four events. Per above, we have added some Event-level features relevant to a tweet-bot: sentence count and character count.

eg %>% select(bullet, nsent, nchar1, Events) %>%
  DT::datatable(rownames = FALSE, 
                options = list(dom = 't',
                               pageLength = nrow(eg),
                               scrollX = TRUE))

In this example, then, Events #3 and #4 will present problems from a character-limit perspective.

Automated tweet-sizing of a thought

locals1$Events <- gsub('([A-Z])(\\.)', '\\1', locals1$Events) 

extract_sent1 <- function(x, chars = 250) { 
  z1 <- data.frame(ts = tokenizers::tokenize_sentences(x)[[1]])
  z1$nchar_sent <- nchar(z1$ts) + 1
  z1$cum_char <- cumsum(z1$nchar_sent)
  z2 <- subset(z1, cum_char < chars)
  paste0(z2$ts, collapse = ' ')
  }

Events <- unlist(lapply(eg$Events, extract_sent1, chars = 250))

data.frame(nsent = tokenizers::count_sentences(Events),
           nchar1 = nchar(Events),
           Events = Events) %>%
  DT::datatable(rownames = FALSE, 
                options = list(dom = 't',
                               pageLength = nrow(eg),
                               scrollX = TRUE))

cumsum_reset <- function(x, thresh = 4) {
    ans <- numeric()
    i <- 0

    while(length(x) > 0) {
        cs_over <- cumsum(x)
        ntimes <- sum( cs_over <= thresh )
        x      <- x[-(1:ntimes)]
        ans <- c(ans, rep(i, ntimes))
        i   <- i + 1
    } 
    return(ans) 
    }

to_thread <- function(x, chars = 250){ # no thread counts at present -- 
  
  x1 <- data.frame(text = unlist(strsplit(x, ' ')))
  x1$chars <- nchar(x1$text) + 1
  x1$cs1 <- cumsum(x1$chars)
  x1$sub_text <- cumsum_reset(x1$chars, thresh = chars)
  
  x2 <- aggregate(x1$text, list(x1$sub_text), paste, collapse = " ")

  x2$ww <- 'm'
  x2$ww[1] <- 'f'
  x2$ww[nrow(x2)] <- 'l'
  
  x2$x <- ifelse(x2$ww %in% c('f', 'm'), paste0(x2$x, ' ...'), x2$x)
  x2$x <- ifelse(x2$ww %in% c('l', 'm'), paste0('... ', x2$x), x2$x)
  paste0(x2$x, collapse = ' || ') 
}

eg1 <- locals1 %>% filter(nchar1 == max(nchar1))

eg2 <- eg1 %>%
  select(Events) %>%
  mutate(Events = to_thread(Events)) %>% ##### --- !!
  
  separate_rows(Events, sep = ' \\|\\| ') 

eg3 <- eg2 %>%
  mutate(Events = paste0(Events, ' [', 
                         row_number(), ' / ', 
                         nrow(eg2), ']'),
         nchar1 = nchar(Events)) %>%
  select(nchar1, Events)

Hashtags sans supervision

A simple unsupervised approach to adding hashtags to tweets based on event content. Here, (1) extract named entities from tweet via the spacyr package, and (character-count depending) (2) add a randomly selected entity as hashtag to tweet.

ent1 <- spacyr::entity_extract(spacyr::spacy_parse(eg2$Events))

ent1 %>% slice(1:5) %>% knitr::kable()

ent2 <- ent1 %>%
  select(doc_id, entity) %>%
  group_by(doc_id) %>%
  distinct() %>%
  mutate(n = n()) %>%
  filter( !(entity == 'Trump' & n !=1) ) %>%
  sample_n(1) %>%
  ungroup() %>%
  mutate(doc_id = as.integer(gsub('text', '', doc_id)),
         entity = paste0('#', gsub("_|,|^the|'s", '', entity)))

eg3 <- eg3 %>%
  mutate(doc_id = row_number()) %>%
  left_join(ent2, by = c('doc_id')) %>%
  mutate(txt3 = ifelse((nchar(Events) + nchar(entity)) > 280, 
                           Events, paste0(Events, ' ', entity)),
         nchar1 = nchar(txt3))

Posting thread using rtweet

We can piece together these different workflows as a simple script to be run daily via cron. The daily script (not detailed here) filters the Event timeline to the day’s date four years ago, and then applies our tweet re-sizing functions to address any potential character-count issues. The code below illustrates how threads are composed based on Event features (ie, sentence and character counts).

rowwise() %>%
mutate (THREAD = case_when (nchar1 < 251 ~ Events, 
                            nchar1 > 250 & nsent > 1 ~ extract_sent1(Events),
                            nchar1 > 250 & nsent == 1 ~to_thread(Events)

Lastly, we build threads by looping through our list of tweet-readied events, replying to each previously posted tweet via the in_reply_to_status_id parameter of the rtweet::post_tweet() function.

rtweet::post_tweet(tred2$txt3[1], token = tk)

if(nrow(tred1) > 1) {
  for(i in 2:length(tred2$txt3)) {
    Sys.sleep(1)
    last_tweet <- rtweet::get_timeline(user = 'MemoryHistoric')$status_id[1]
    
    rtweet::post_tweet(tred2$txt3[i],
                       in_reply_to_status_id = last_tweet,
                       token = tk) } 
} else {NULL}

Summary

See and follow MemoryHistoric on Twitter!