Exeunt Android; Enter Ads: An Update on the Sources of Presidential Tweetage

 

A quick update as something I consider interesting has emerged from the ongoing archiving of the, er, current ‘Trumpian’ tweetage (see a previous post here). In case you do follow this blog you may be aware I’ve been keeping an eye on the ‘source’ of the Tweets, which is information (a metadata field) pertaining to each published Tweet which is made publicly visible by Twitter to anyone through certain applications like TweetDeck and directly through Twitter’s API (for Twitter’s ‘Field Guide’, see this).

Given the diversity of sources detected on the Tweets from the account under scrutiny in the past, hypotheses have been proposed suggesting correlations between type of content and source (application used to post Tweets); others have suggested that it is also indication of different people behind the account (though as we have said previously it is also possible that the same person tweets from different devices and applications).

Anyway, here’s some recent new insights emerging from the data since the last post:

  • Since Inauguration Day (20 January 2017), the last Tweet coming from Twitter for Android so far was timestamped 25/03/2017 10:41 (AM; DC time).  No Tweets from Android have been posted since that Tweet until the time of writing of this.
  • The last Tweet coming from the Twitter Web Client so far was timestamped 25/01/2017  19:03:33. No more Tweets with the Web Client as source have been posted (or collected by my archive) since then.
  • Since Inauguration Day, the Tweet timestamped 31/03/2017  14:30:38 was the first one to come from Twitter Ads. Since then 21 Tweets have been posted from Twitter Ads, the last one so far timestamped 17/05/2017  16:36:02.
  • During April and May 2017 Tweets have only come from Twitter for iPhone or Twitter Ads. The account in question has tweeted every sincle day throughout May until today 18 May 2017, a total of 90 Tweets so far (including a duplicated one in which a typo was corrected). Below a breakdown per source:

 

from_user Month Source Count
realDonaldTrump May Twitter for iPhone 81
Twitter Ads 9

 

As a keen Twitter user I personally find it interesting Twitter for Android has stopped being used by the account in question and that Twitter Ads has been used recently (instead?) in alternation with the Tweets from iPhone. Eyeballing the dataset quickly appears to indicate there might be a potential correlation between Tweets with links and official announcements (rather than statements/opinions) and Twitter Ads, but that requires looking into more closely and I will have to leave that for another time.


*Public note to self: I need to get rid of this habit of capitalising ‘Tweets’ as a noun… it becomes annoying.

Android vs iPhone: Source Counts and Trends in a Bit More than a Year’s Worth of Trumpian Tweetage

Last month I took a quick look at a month’s worth of Trumpian tweetage (user ID 25073877)  using text analysis. Using a similar methodology I have now prepared and shared a CSV file containing Tweet IDs and other metadata of 3,805 Tweets  from user ID 25073877 posted publicly between Thursday February 25  2016 16:35:12 +0000  to Monday April 03 2017 12:51:01 +0000. I deposited the file on figshare, including notes on motivation and methodology, here:

3805 Tweet IDs from User 25073877 [Thu Feb 25 16:35:12 +0000 2016 to Mon Apr 03 12:51:01 +0000 2017].

figshare. https://doi.org/10.6084/m9.figshare.4811284.v1

The dataset allows us count the sources for each Tweet (i.e. the application used to publish each Tweet according to the data provided by the Twitter Search API). The resulting counts are:

Source Tweet Count
Twitter for iPhone 1816
Twitter for Android 1672
Twitter Web Client 287
Twitter for iPad 22
Twitter Ads 3
Instagram 2
Media Studio 2
Periscope 1

As we have seen in previous posts, the account has alternated between iPhone and Android since the Inauguration. I wanted to look at relative trends throughout the dataset. Having prepared the main dataset I performed the text analysis of a document comprising the source listing arranged in chronological order according to the date and time of Tweet publication, and the listing corresponds to Tweets published between 25 February 2016 and Monday 3 April 2017. Using the Trends tool in Voyant, I divided the document in 25 segments, with the intention to roughly represent each monthly period covered in the listing and highlight source relative frequency trends in the period covered per segment.

The Trends tool shows a line graph depicting the distribution of a word’s occurrence across a corpus or document; in this case each word represents the source of a Tweet in the document. Each line in the graph is coloured according to the word it represents, at the top of the graph a legend displays which words are associated with which colours. I only included the most-used sources, leaving iPad there as reference.

The resulting graph looks like this:

Line Graph of Relative Frequencies of four most used sources by realdonaldtrump visualised in 25 segments of a document including Twe3,805 Tweets  from user ID 25073877 posted publicly between Thursday February 25  2016 16:35:12 +0000  to Monday April 03 2017 12:51:01 +0000. Data collected and analysed by Ernesto Priego. CC-BY. Chart made with Trends, Voyant Tools by Stéfan Sinclair & Geoffrey Rockwell (CC-BY 2017).
Line graph of the relative frequencies of the four most used sources visualised in 25 segments of a document including 3,805 Tweets from user ID 25073877 dated between Thursday February 25 2016 16:35:12 +0000 and Monday April 03 2017 12:51:01 +0000. Data collected and analysed by Ernesto Priego. CC-BY. Chart made with Trends, Voyant Tools by Stéfan Sinclair & Geoffrey Rockwell (CC-BY 2017).

I enjoyed this article by Christopher Ingraham (Washington Post Weblog, 3 April 2017), and I envy the access to the whole Trupian tweetage dataset, that would be essential to attempt to reproduce the analysis presented. The piece focuses on the use of exclamation marks (something I took an initial look at on my 6 February 2017 post), but it would be useful to take a closer look at any potential significant correlations between use of language in specific Tweets and the sources used to post those Tweets.

The article also has an embedded video titled ‘When it’s actually Trump tweeting, it’s way angrier’, repeating claims that there is a clear difference between those Tweets the account in question published from an iPhone and those published from an Android. I briefly referred to this issue on my 15 March 2017 post already, and I have not seen evidence yet that it is a staffer who actually posts from Twitter for iPhone from the account. I may be completely wrong, but I am still not convinced there is data-backed evidence to say for certain that Tweets from different sources are always tweeted by two or more different people, or that the differences in language per source are predictable and reliably attributable to a single specific person (the same people can after all tweet from the same account using different devices and applications, and indeed potentially. use different language/discourse/tone).  Anecdotal, I know, but I have noticed that sometimes my tweetage from the Android mobile app is different from my tweetage from TweetDeck on my Mac, but no regular patterns can be inferred there.

I do not necessarily doubt there is more than one person using the account, nor that the language used may vary significantly depending on the Tweets’ source.  What I’d like to see however is more robust studies demonstrating and highlighting correlations between language use in Tweets- texts and Tweets’ sources from the account in question taking into consideration that the same users can own different devices and use different language strategies depending on a series of contextual variables. Access to the source data of said studies should be consider essential for any assessment of any results or conclusions provided. Limitations and oppostion to more open sharing of Twitter data for research reproducibility are just one hurdle on the way for more scholarship in this area.

Textal, a UCLDH Text Analysis App, ¡también en Español!

textal1

“Textal is a free smartphone app, currently available on iOS, that allows you to analyze websites, tweet streams, and documents, as you explore the relationships between words in the text via an intuitive word cloud interface. You can generate graphs and statics, as well as share the data and visualizations in any way you like. Textal is a fun and useful tool for research and play.”

It was developed by Melissa Terras, Steven Gray and Rudolf Ammann, from the University College London Centre for Digital Humanities.

I did the Spanish translation of the app’s functions, commands and messages; (¡si encuentran errores por favor díganme!). Translating software ‘manually’ (that is, without translation software) is always complicated, not least because some terms that have been accepted in English do not really exist yet in Spanish, or sound odd, or are simply left in Enlgish. Hopefully the Spanish in the app reads well to Spanish-speaking users.

Adeline Koh has written a cool “first look” at Textal for ProfHacker at the Chronicle of Higher Education, here.

If you have an iOS device, give Textal a go!