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The South California/Indiana @Botometer is based on a training data that doesn't contain #socialbot, but "content polluters" aka "spammers" & "malware disseminators".
Their method misclassified 12% of Nobel Laureates as bots and 60.7% of the Botwiki bots as humans.
The Berkeley/Swansea team claims that during the 2016 US elections, #socialbot shifted the results by 3.23% towards Trump, based on a correlation between the amounts of tweets using various political hashtags & vote results.
What they forgot: Correlation doesn’t mean causality!
We need to ask ourselves how the totally unfunded #socialbot theory spread so widely.
And more generally, we need to ask ourselves how and why academic research teams spread wrong theories based on scientifically flawed clickbait research.
We need to ask ourselves why journalists base their reporting on studies whose results contradict each other, and don't even question if these theories produce accurate results.
How do you explain that the tool you reported on classifies 1/2 of the US congress as #socialbot?
We need to ask ourselves why we pass new laws based on unreviewed research published by a few scientists, and what that means for our democracy.
Academic research teams, review the #socialbot papers.
Journalists and lawmakers, don't base your reporting and laws on wrong research claims.
Do you have more info? Get in touch!https://michaelkreil.github.io/openbots/
It is important to look for explanations for the main political events of the last years. And we should keep doing so.
But the research needs to be accurate, reviewed, and reproducible. The #socialbot theory isn't.
I call #bullshit!
Rigorous causal inference is very much needed in this area. Maybe you've seen this piece where @sinanaral & I outline how such work could be done. So far, it really has not...
https://twitter.com/sinanaral/status/1167142057724563456 …
https://science.sciencemag.org/content/365/6456/858.full …
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