In dataknut/hashTagR: Gets, saves and processes tweets and hashtags

knitr::opts_chunk$set(echo = FALSE) # do not echo code
knitr::opts_chunk$set(warning = FALSE)
knitr::opts_chunk$set(message = FALSE)
knitr::opts_chunk$set(fig_caption = TRUE)
knitr::opts_chunk$set(fig_height = 6) # default, make it bigger to stretch vertical axis
knitr::opts_chunk$set(fig_width = 8) # full width
knitr::opts_chunk$set(tidy = TRUE) # tidy up code in case echo = TRUE

# Set start time ----
startTime <- proc.time()

# Libraries ----
library(dkUtils)

# additional libs required by this code
reqLibs <- c("ggplot2",  
             "plotly",
             "readr", 
             "reshape2",
             "stringr",
             "knitr", 
             "kableExtra",
             "hashTagR"
             )

print(paste0("Loading the following libraries: ", reqLibs))
# Use Luke's function to require/install/load
dkUtils::loadLibraries(reqLibs)

dPath <- "~/Data/twitter/"
hashTags <- params$hashTags
searchStrng <- params$searchString
timeZone <- "Pacific/Auckland"

TL;DR

Just a bit of dataknut fun woven around the day job.

You'll be wanting Section \@ref(cumulative) for the trending hashtags...

Terms of re-use

License

CC-BY unless otherwise noted.

Citation

• Anderson, B (r lubridate::year(today())) Twitter hashtag analysis: r searchString. Downloaded from: r params$pubUrl.

Code

See:

• Code: https://github.com/dataknut/hashTagR/blob/master/analysis/genericHashTagReport.Rmd
• Code/report history
• Raise an issue here

Purpose

The idea is to extract and visualise tweets and re-tweets of r searchString (see r params$explHashTag).

Why? Err.... Just. Because.

How it works

Code borrows extensively from https://github.com/mkearney/rtweet

The analysis used rtweet to ask the Twitter search API to extract 'all' tweets containing the r searchString hashtags in the 'recent' twitterVerse.

It is therefore possible that not quite all tweets have been extracted although it seems likely that we have captured most recent human tweeting which was the main intention. Future work should instead use the Twitter streaming API.

# load from pre-collected ----
message("Load from pre-collected data and check for duplicates")

raw_twDT <- hashTagR::loadTweets(dPath, searchString) # we like data.tables

# for testing
# raw_twDT <- hashTagR::loadTweets(dPath, searchString) # we like data.tables


# this data.table will have duplicate entries because:
# 1. we may have run the search mutiple times
# 2. the search results are dynamic - they can change if a tweet is liked, re-tweeted etc as this data is updated

# we don't want duplicates, we just want the most recent tweet record by time of creation and screen_names

rn <- nrow(raw_twDT)
twDT <- unique(raw_twDT, fromLast = TRUE, by = c("created_at", "screen_name") ) # drop duplicates
un <- nrow(twDT)
message("Returning ", dkUtils::tidyNum(un),
        " tweets after dropping ",
        dkUtils::tidyNum(rn - un),
        " duplicates.")

twDT <- twDT[, created_at_local := lubridate::with_tz(created_at, tzone = timeZone)]
twDT <- twDT[, ba_obsDate := lubridate::date(created_at_local)]
twDT <- twDT[, ba_obsTime := hms::as.hms(created_at)] # this will auto-convert to local time

The data has:

• r tidyNum(nrow(twDT)) tweets (including r tidyNum(nrow(twDT[is_quote == "TRUE"])) quotes and r tidyNum(nrow(twDT[is_retweet == "TRUE"])) re-tweets)
• from r tidyNum(uniqueN(twDT$screen_name)) tweeters
• between r min(twDT$created_at_local) and r max(twDT$created_at_local) (r timeZone).

Analysis

Tweets and Tweeters over time

dataCap <- paste0("Source: Data collected from Twitter's REST API via rtweet",
                  "\nAll (re)tweets and quotes containing ", searchString, 
                    " from ",
                      min(twDT$created_at_local),
                    " to ",
                    max(twDT$created_at_local),
                  " (", timeZone, ")"
                    )

twDT <- twDT[, ba_tweetType := "Tweet"]
twDT <- twDT[is_retweet == TRUE, ba_tweetType := "Re-tweet"]
twDT <- twDT[is_quote == TRUE, ba_tweetType := "Quote"]
twDT <- twDT[, obsDateHour := lubridate::floor_date(created_at_local, "hour")]

plotDT <- twDT[, .(
                 nTweets = .N,
                 nTweeters = uniqueN(screen_name)
               ), keyby = .(obsDateHour, ba_tweetType)
               ]

myPlot <- ggplot2::ggplot(plotDT, aes(x = obsDateHour)) +
    geom_line(aes(y = nTweets, colour = "N tweets")) +
    geom_line(aes(y = nTweeters, colour = "N tweeters")) +
    facet_grid(ba_tweetType ~ .) +
    scale_x_datetime(breaks ="4 hours", date_labels = "%c") +
    theme(strip.text.y = element_text(size = 9, colour = "black", angle = 90)) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5)) +
    theme(legend.position = "bottom") +
    theme(legend.title = element_blank()) +
    labs(caption = dataCap,
         x = "Date",
         y = "Count"
    )

myPlot

Figure \@ref(fig:allDaysChart) shows the number of tweets and tweeters in the data extract by day. The quotes, tweets and re-tweets have been separated.

If you are in New Zealand and you are wondering why there are no tweets today (r lubridate::today()) the answer is that twitter data (and these plots) are working in UTC and (y)our today() may not have started yet in UTC. Don't worry, all the tweets are here - it's just our old friend the timezone... :-)

Who's tweeting?

Next we'll try by screen name.

plotDT <- twDT[, 
                    .(
                      nTweets = .N
                    ), by = .(screen_name, ba_obsDate)]

tilePlot <- ggplot(plotDT, aes(x = ba_obsDate)) +
    geom_tile(aes(y = screen_name, fill = nTweets)) +
    theme(strip.text.y = element_text(size = 9, colour = "black", angle = 0)) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5)) +
    #scale_x_reverse() + # fix reverse plotting of long
    scale_x_date(date_breaks = "1 day", date_labels = "%a %d %b %Y") +
    scale_fill_gradient(low="green", high = "red") +
    theme(legend.position = "bottom") +
    theme(legend.title = element_blank()) +
    labs(caption = dataCap,
         x = "Date",
         y = "Screen name"
    )
tilePlot

Figure \@ref(fig:screenNamesTile) is a really bad visualisation of all tweeters tweeting over time. Each row of pixels is a tweeter (the names are probably illegible) and a green dot indicates a few tweets in the given day while a red dot indicates a lot of tweets.

So let's re-do that for the top 50 tweeters so we can see their tweetStreaks (tm)...

Top tweeters:

allTweetersDT <- twDT[, .(nTweets = .N), by = screen_name][order(-nTweets)]

kableExtra::kable(caption = "Top 15 tweeters (all days)", 
                  head(allTweetersDT, 15)) %>%
  kable_styling()

And their tweetStreaks are shown in Figure \@ref(fig:screenNameTop50)...

myDataCap <- paste0(dataCap,
                    "\nScreen names in reverse alphabetical order"
                          )

matchDT <- head(allTweetersDT,50)
matchDT <- matchDT[, maxT := nTweets]
setkey(matchDT, screen_name)
setkey(twDT, screen_name)

tempDT <- merge(twDT, matchDT)

plotDT <- tempDT[matchDT, 
                    .(
                      nTweets = .N
                    ), keyby = .(maxT,screen_name,ba_obsDate)]

plotDT <- plotDT[order(plotDT$maxT,plotDT$screen_name)]

myPlot <- ggplot(plotDT, aes(x = ba_obsDate)) +
    geom_tile(aes(y = screen_name, fill = nTweets)) +
    theme(strip.text.y = element_text(size = 9, colour = "black", angle = 0)) +
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5)) +
    scale_x_date(date_breaks = "1 day", date_labels = "%a %d %b %Y") +
    scale_fill_gradient(low="green", high = "red") +
    theme(legend.position = "bottom") +
    theme(legend.title = element_blank()) +
    labs(caption = myDataCap,
         x = "Date",
         y = "Screen name"
    )

myPlot 

Any twitterBots...?

Which hashtags are mentioned the most?

This is very quick and dirty but... to calculate this we have to do a bit of string processing first.

htDT <- twDT[!is.na(hashtags), .(hashtags, ba_obsDate, ba_tweetType)] # remove any tweets without hashtags. How can there be no hashtags when we searched on hashtags?

# now string split them
# https://stackoverflow.com/questions/33200179/dynamically-assign-number-of-splits-in-data-table-tstrsplit
splits <- max(lengths(strsplit(htDT$hashtags, "|", , fixed=T)))
htDT <- htDT[, paste0("ht", 1:splits) := tstrsplit(hashtags, "|", fixed=T)]
# reshape the list
htLongDT <- reshape2::melt(htDT, id=c("hashtags","ba_obsDate", "ba_tweetType"))
# remove NA
htLongDT <- htLongDT[!is.na(value)]
message("We have ", nrow(htLongDT), " hashtags.")
message("That's about ~ ", round(nrow(htLongDT)/nrow(twDT),2), " hashtags per tweet...")
# process in steps to be clear
htLongDT <- htLongDT[, htOrig := value]

This is how I have tidied the hashtags (make other suggestions here):

# First we make everything lower case
htLongDT <- htLongDT[, htLower := tolower(htOrig)] # lower case

# Next we remove the macrons just in case
# h/t: https://twitter.com/Thoughtfulnz/status/1046685305569345536
htLongDT <- htLongDT[, htClean := stringr::str_replace_all(htLower,"[āēīōū]",dkUtils::deMacron)]

# we might need to do other things here depending on the the context

Table \@ref(tab:tweetTable) shows the total count of each #hashtag by (re)tweet type.

kableExtra::kable(caption = "Top 20 hashtags",
                  head(htLongDT[, .(count = .N), by = .(hashTag = htClean, type = ba_tweetType)][order(-count)],20)) %>%
  kable_styling()

threshold <- 100

Figure \@ref(fig:plotHashtags) plots the daily occurence of these hashtags after removing variants of r searchString and selecting only those which have more than r threshold mentions on any day. For clarity tweets and re-tweets are aggregated. See Section \@ref(problems) for the problems with this #hashTag counting approach.

# remove the hashtags we searched for
testDT <- htLongDT

for(n in 1:length(hashTags)){
  testDT <- testDT[!(grepl(hashTags[n],htClean,ignore.case = TRUE))]
}


htTabDT <- testDT[, .(count = .N), keyby = .(ba_obsDate, htClean, ba_tweetType)]

ggplot2::ggplot(htTabDT[count > threshold], aes(x = ba_obsDate, y = htClean, fill = count)) +
  geom_tile() + 
  theme(strip.text.y = element_text(size = 9, colour = "black", angle = 0)) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5)) +
  scale_x_date(date_breaks = "1 day", date_labels = "%a %d %b %Y") +
  scale_fill_gradient(low="green", high = "red") +
 # facet_grid(ba_tweetType ~ .) +
  theme(legend.position = "bottom") +
  theme(legend.title = element_blank()) +
  labs(caption = paste0(dataCap,"\nReverse alphabetical order"),
       x = "Date",
       y = "Unique hashtags"
  )

Location (lat/long)

We wanted to make a nice map but sadly we see that most tweets have no lat/long set.

plotDT <- twDT[, 
                    .(
                      nTweets = .N
                    ), by = .(geo_coords)]
kable(cap="All logged lat/long values",
      plotDT)

plotDT <- twDT[, 
                    .(
                      nTweets = .N
                    ), by = .(coords_coords)]
kable(cap="All logged coord values",
      plotDT)

Location (textual)

This appears to be pulled from the user's profile although it may also be a 'guestimate' of current location.

Top country locations for tweets:

t <- twDT[, .(nTweets = .N), by = location][order(-nTweets)]
kable(caption = "Top 15 locations for tweeting",
      head(t, 15)
      )

Top locations for tweeters:

t <- twDT[, .(nTweeters = uniqueN(screen_name)), by = location][order(-nTweeters)]
kable(caption = "Top 15 locations for tweeters",
      head(t, 15)
      )

Now try the full place name - rarely available.

t <- twDT[, .(nTweets = .N), by = place_full_name][order(-nTweets)]
kable(caption = "Top 15 locations for tweeting",
      head(t, 15)
      )

Most popular hashtags over time {#cumulative}

There are a lot of problems with this approach (see Section \@ref(problems)) but Figure \@ref(fig:cumulativeHtCounts) shows trends over time (watch for lines of apparently dis-similar hashtags where the macron fix has failed) and Figure \@ref(fig:htCountsTodate) shows the totals to date.

Figure \@ref(fig:cumulativeHtCounts) uses plotly to avoid having to render a large legend - just hover over the lines to see who is who...

plotDT <- htTabDT[, .(count = sum(count)), keyby = .(ba_obsDate,htClean)]
plotDT <- plotDT[, cumCount := cumsum(count), by = htClean]
plotDT <- plotDT[, maxCount := max(cumCount), by = htClean] # so we can filter

linePlot <- ggplot2::ggplot(plotDT[maxCount > threshold], aes(x = ba_obsDate, y = cumCount, colour = htClean)) +
  geom_line() + 
  theme(strip.text.y = element_text(size = 9, colour = "black", angle = 0)) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5)) +
  scale_x_date(date_breaks = "1 day", date_labels = "%a %d %b %Y") +
  theme(legend.position = "none") +
  theme(legend.title = element_blank()) +
  labs(caption = dataCap,
       x = "Date",
       y = "Cumulative count per day"
  )

plotly::ggplotly(linePlot)

plotDT <- htTabDT[, .(count = sum(count)), keyby = .(htClean)]

plotDT <- plotDT[, htCleanOr := reorder(htClean, -count)]

ggplot2::ggplot(plotDT[count > threshold], aes(x = htCleanOr, y = count, fill = htCleanOr)) +
  geom_col() + 
  theme(strip.text.y = element_text(size = 9, colour = "black", angle = 0)) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 0.5)) +
  theme(legend.position = "none") +
  theme(legend.title = element_blank()) +
  labs(caption = dataCap,
       x = "hashtag",
       y = "Count to date"
  )

Problems {#problems}

Loads of them. But primarily:

• hashTags as indicators:
  • tweets mentioning a topic without using a hashtag will not show up so;
  • there is no sentiment analysis;
• removing the macrons: the tables and plots may display incorrect Māori words. Unfortunately I have not found a way to put them back in the right words (and only the right words).

About

As ever, #YMMV.

t <- proc.time() - startTime

elapsed <- t[[3]]

Analysis completed in r elapsed seconds ( r round(elapsed/60,2) minutes) using knitr in RStudio with r R.version.string running on r R.version$platform.

A special mention must go to https://github.com/mkearney/rtweet [@rtweet] for the twitter API interaction functions.

Other R packages used:

• base R - for the basics [@baseR]
• data.table - for fast (big) data handling [@data.table]
• ggplot2 - for slick graphs [@ggplot2]
• plotly - for interactive graphs [@plotly]
• readr - for nice data loading [@readr]
• reshape2 - wide to long [@reshape2]
• rtweet - twitter API search [@rtweet]
• stringr - processing strings [@stringr]
• knitr, bookdown & kableExtra - to create this document [@knitr,@bookdown, @kableExtra]

References

dataknut/hashTagR documentation built on Sept. 20, 2023, 7:24 a.m.