(Reader: Rishi) An AI-based Analysis of Tweets vs. News


Reader Post | By Rishi is a web app I built that measures emotions in tweets vs. in news headlines. A full breakdown of the statistical methodology used is available on the website, but in short, emotions are classified into categories of: fear, happy, sad, angry, excited, bored, and scores for them are automatically calculated (the output being a score of 0–100) on a daily basis by taking a random sampling of the most popular trending topics and tweets associated with them. AI-based NLP (natural language processing) techniques are used to then obtain emotion scores for the 6 aforementioned common emotions. By comparing the scores over a 1 month period, I found that tweets and news headlines follow almost exactly the same trends. This seems to go along with the popular line of thinking that Twitter is currently overrun by bots and fake accounts, with experts suggesting it could be over 80% of users. This supports the conclusion that the same sources that drive MSM narratives are steering the overall “conversation” on Twitter. The most surprising thing I found, however, was that privately run blogs do NOT follow the same trends as mainstream news at all. In fact I see basically no correlation. To me, this proves something is definitely happening at Twitter, whether it’s intentional or not. I wrote an article explaining all of this in detail, including a full technical and statistical methodology, along with graphs which support my conclusions:

Or if you don’t like’s paywall (though it does let you read several articles a month for free), here’s a link that bypasses it:

If this is a topic that interests you and you’d like to discuss it further, feel free to email rishi[at]



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