Infodemiology: Language of the Twitter Bot

Submitted by alexmorton on Thu, 01/30/2020 - 09:03
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Submitted by Mason Laney on Thu, 01/30/2020 - 20:24

I thought that the report was really good!

I found the format of the report (academic -> condensed academic -> new tweet -> old tweet) to be a very unique and creative way to demonstrate the limitations and characteristics of Twitter. As the report got more and more condensed, the language seemed to get increasingly strong and divisive ("infuriating", "suck", etc). As a result, I found that the shorter reports/tweets were telling you how you should feel about the content, whereas the full academic report let the reader draw more of their own conclusions from the data itself. I thought that this did a good job of proving the point that the limited nature of Twitter makes it more divisive.

The only source of confusion for me was the first diagram, as I was initially unsure how I was supposed to read it and what conclusions I was supposed to draw from it. Apart from that, I found the report clear and straightforward.

After reading the report, I would be interested in learning more about initiatives and programs designed to recognize bots. How effective are they? Is Twitter actively doing anything to reduce the number of bots on the site, or are they content with their existence and more focused on the misinformation part?

For my own report, I may emulate the use of charts and the general tone (which is slightly less formal than my original tone).

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