What’s in a month’s worth of presidential tweetage?

I prepared a dataset containing a total of 123 public Tweets and corresponding metadata from user_id_str 25073877 between 15 February 2017 06:40:32 and 15 March 2017  08:14:20 Eastern Time (this figure does not factor in any tweets the user may have deleted shortly after publication). Of the 123 Tweets 68 were published from Android; 55 from iPhone. The whole text of the Tweets in the dataset accounts for 2,288 words, or 12,364 characters (no spaces; including URLs).

Using the Trends tools from Voyant Tools by Stéfan Sinclair & Geoffrey Rockwell I visualised the raw frequencies of the terms ‘Android’ and ‘iPhone’ in this dataset over 30 segments (more or less corresponding to the length of the month covered in the dataset) where each timestamped Tweet, sorted in chronological order, had its corresponding source indicated.

The result looked like this:

Raw frequency of Tweets per source in 30 segments by realdonaldtrump between 15 February 2017 06:40:32 and 15 March 2017 08:14:20 Eastern Time. Total: 123 Tweets: 68 from Android; 55 from iPhone. Data collected and analysed by Ernesto Priego. CC-BY. Chart made with Trends, Voyant Tools by Stéfan Sinclair & Geoffrey Rockwell (CC 2017).
Raw frequency of Tweets per source in 30 segments by realdonaldtrump between 15 February 2017 06:40:32 and 15 March 2017 08:14:20 Eastern Time. Total: 123 Tweets: 68 from Android; 55 from iPhone. Data collected and analysed by Ernesto Priego. CC-BY. Chart made with Trends, Voyant Tools by Stéfan Sinclair & Geoffrey Rockwell (CC 2017).

The chart does indeed reflect the higher number of Tweets from Android, and it also shows how over the whole document both sources are, in spite of more frequent absences from Tweets from iPhone, present throughout. The question as usual is what does this tell us. Back in 9 August 2016 David Robinson published an insightful analysis where he concludes that “he [Trump] writes only the (angrier) Android half”. With the source data I have gathered so far it would be possible (given the time and right circumstances) to perform a content analysis of Tweets per source, in order to confirm or reject any potential corelations between types of Tweets (re: tone, function, sentiment, time of day) and source used to post them.

Eyeballing the data, specifically since Inauguration Day until the present, does not seem to provide unambiguous evidence that the Tweets are undoubtedly written by two different persons (or more). What it is factual is that the Tweets do come from different sources (see my previous post), but at the moment, like with everything else this administration has been doing, my cursory analysis has only found conflicting insights, where for example a Tweet one would perhaps have expected to have been posted from iPhone (attributable hypothetically to a potentially less inflammable aide) was in fact posted from Android, and viceversa.

I may be wrong, but at the moment I cannot see any evidence there is any kind of predictable pattern, let alone strategy, behind the alternation between Android and iPhone (the only two type of sources used to publish Tweet from the account in question in the last month). Most of the times Tweets by source type will come in sequences of four or more Tweets, but sometimes a random lone Tweet from a different source will be sandwiched in between.

More confunsigly, all of the Tweets published between 08/03/2017 18:50 and 15/03/2017  08:14:20 have only had iPhone as source, without exception. Attention to detail is required to run robust statistical and content analyses that consider complete timestamps and further code the Tweet text and time data into more discrete categories, attempting a high level of granularity at both the temporal (time of publishing; ongoing documented events) and textual (content; discourse) levels. (If you are reading this and would like to take a look at the dataset, DM me via Twitter).

Anyway. In case you are curious, here’s the top 20 most frequent words in the text of the tweets, per source, in this dataset ( 15 February 2017 06:40:32 and 15 March 2017  08:14:20 Eastern Time). Analysis courtesy of Voyant Tools, applying a customised English stop words list (excluding Twitter-specific terms like rt, t.co, https, etc, but leaving terms in hashtags).

Android iPhone
Term Count Trend Term Count Trend
fake 11 0.007795889 great 16 0.016129032
great 11 0.007795889 jobs 14 0.014112903
media 10 0.007087172 america 6 0.006048387
obama 10 0.007087172 trump 6 0.006048387
election 9 0.006378455 american 5 0.005040322
just 9 0.006378455 join 5 0.005040322
news 9 0.006378455 big 4 0.004032258
big 8 0.005669738 healthcare 4 0.004032258
failing 6 0.004252303 meeting 4 0.004032258
foxandfriends 6 0.004252303 obamacare 4 0.004032258
president 6 0.004252303 thank 4 0.004032258
russia 6 0.004252303 u.s 4 0.004032258
democrats 5 0.003543586 whitehouse 4 0.004032258
fbi 5 0.003543586 address 3 0.003024194
house 5 0.003543586 better 3 0.003024194
new 5 0.003543586 day 3 0.003024194
nytimes 5 0.003543586 exxonmobil 3 0.003024194
people 5 0.003543586 investment 3 0.003024194
white 5 0.003543586 just 3 0.003024194
american 4 0.002834869 make 3 0.003024194
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