Great! News! People! Fake! Donald’s Tweets: 18 January 2017 to 18 January 2018

Trump Simplest Words image Image via The Telegraph
Image via The Telegraph

In two days it will be a year since the inauguration of Twitter user ID 25073877.  Time flies when things are beyond ridiculous, right?

Some of you may remember I’ve published before other posts looking into various aspects of this user’s tweetage. I have already detailed the methodology I have followed (as well as its acknowledged limitations) on some of those previous posts. This has been a work in progress. See for example this, or this, or even this. There’s more if you follow the links.

Anyway, as the anniversary of the inauguration approaches I wanted to share with you, for what it’s worth, some quick numbers from a whole year’s worth of Twitter data.

The dataset I worked with for the purpose of this post is based on a larger Twitter archive I’ve been collecting and studying.

The dataset that I looked into in this occasion is composed by 2,587 tweets posted between 18/01/2018 08:49 AM EST (GMT -5) and 18/01/2017 06:53 AM EST (GMT-5).

As usual I did some basic text analysis, and some quick comparative quant stuff.

20 Most Tweeted Terms

Term Count
great 473
news 190
people 182
fake 166
thank 162
just 160
today 158
president 151
big 145
tax 140
trump 137
america 134
country 128
u.s 125
jobs 116
american 115
time 110
foxandfriends 98
media 98
new 97


Other Twitter Data Numeralia

Twitter Text Counts

Number of ! 1,261
Number of Characters (no spaces, including URLs and usernames) 275,964
Number of Pages (single space, 12pt) 109
Number of Words 50,176

Follower Growth

User followers as of  18/01/2018 08:49 46,815,170
User followers as of 18/01/2017 06:53 20,227,768
Gained followers in the period 26,587,402

Tweets About the Mexico Border Wall

id_str time (EST)
9.53979E+17 18/01/2018 08:16
9.53264E+17 16/01/2018 08:54
9.51229E+17 10/01/2018 18:07
9.50884E+17 09/01/2018 19:16
9.49066E+17 04/01/2018 18:53
9.46732E+17 29/12/2017 08:16
9.38391E+17 06/12/2017 07:53
9.20425E+17 17/10/2017 19:03
9.18063E+17 11/10/2017 06:36
9.08274E+17 14/09/2017 06:20
9.01803E+17 27/08/2017 09:44
8.97833E+17 16/08/2017 10:51
8.97045E+17 14/08/2017 06:38
8.85279E+17 12/07/2017 19:24
8.78014E+17 22/06/2017 18:15
8.56849E+17 25/04/2017 08:36
8.56485E+17 24/04/2017 08:28
8.56172E+17 23/04/2017 11:44
8.56171E+17 23/04/2017 11:42
8.30406E+17 11/02/2017 08:18
8.24617E+17 26/01/2017 08:55
8.24084E+17 24/01/2017 21:37
8.23147E+17 22/01/2017 07:35

[hydrate tweets using twarc]

The susual caveats apply. Numbers must be taken with a pinch of salt: the Twitter Search API is not a complete index of all Tweets, but instead an index of recent Tweets– my archive has collected Tweets every hour, which means, for instance, that Tweets that are promptly deleted in between collections do not get archived.

I have attempted refining the dataset, but duplicated Tweets might have stubbornly survived, which in turn logically would have affected the counts. However, in spite of these limitations, the data is indicative and potentially useful and/or interesting as documentation of current and recent historical events. For what it’s worth.

We’ve lived with this user’s tweets daily, and we are very much aware of the kind of discourse developed through the constant, reliably exasperating tweetage. So these basic numbers are most likely not to tell you anything you weren’t aware of already. A simile occurs to me: we are all aware of the daily, accumulative effects of stress, or, say, ageing, but sometimes it is only until we compare snapshots that we realise the true extent of its effects.