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Actor and writer David Mills ’s one-person dramatic rendition of Langston Hughes ’s poems and short stories journeys through the Harlem Renaissance—from the 6975s through the 6965s. Mills portrays Hughes’s notable characters. I don’t normally post about politics (I’m not particularly savvy about polling, which is where data science ). Every hyperbolic tweet is from Android (from him). When he’s insulting a rival, he’s usually tweeting from an Android. Is this an artifact showing which tweets are Trump’s own and which are by some handler? Others have and noticed this tends to hold up- and Trump himself. But how could we examine it quantitatively?

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I’ve been writing about text mining and recently, particularly during my development of the with Julia Silge, and this is a great opportunity to apply it again. First we’ll retrieve the content of Donald Trump’s timeline using the userTimeline function in the package: We clean this data a bit, extracting the source application. One consideration is what time of day the tweets occur, which we’d expect to be a “signature” of their user. Another place we can spot a difference is in Trump’s anachronistic behavior of “manually retweeting” people by copy-pasting their tweets, then surrounding them with quotation marks::

stay the course mr trump your message is resonating with the PEOPLE In the remaining by-word analyses in this text, I’ll filter these quoted tweets out (since they contain text from followers that may not be representative of Trump’s own tweets). Somewhere else we can see a difference involves sharing links or pictures in tweets. They totally distort so many things on purpose. Crimea, nuclear, the baby and so much more. Very dishonest!

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Now that we’re sure there’s a difference between these two accounts, what can we say about the difference in the content? We’ll use the package that and I developed. We start by dividing into individual words using the unnest_tokens function (see for more), and removing some common “stopwords”: These should look familiar for anyone who has seen the feed. ”)A lot of “emotionally charged” words, like “badly”, “crazy”, “weak”, and “dumb”, were overwhelmingly more common on Android.

This supports the original hypothesis that this is the “angrier” or more hyperbolic account. (The positive emotions weren’t different to a statistically significant extent). We’re especially interested in which words drove this different in sentiment. I was fascinated by the recent about Tony Schwartz, Trump’s ghostwriter for The Art of the Deal. Of particular interest was how Schwartz imitated Trump’s voice and philosophy:

In his journal, Schwartz describes the process of trying to make Trump’s voice palatable in the book. It was kind of “a trick, ” he writes, to mimic Trump’s blunt, staccato, no-apologies delivery while making him seem almost boyishly appealing…. Looking back at the text now, Schwartz says, “I created a character far more winning than Trump actually is. Bad reporting- no money, no cred! Failing will always take a good story about me and make it bad.

Every article is unfair and biased.

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