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On 15 September 2020, the Washington Post published an article by Isaac Stanley-Becker titled “Pro-Trump youth group enlists teens in secretive campaign likened to a ‘troll farm,’ prompting rebuke by Facebook and Twitter.” The article reported on a preliminary analysis we conducted at the request of The Post. Here we would like to share the dataset used in our analysis with the research community. Our Observatory on Social Media at Indiana University has been studying social media manipulation and online misinformation for over ten years. We uncovered the first known instances of astroturf campaigns, social bots, and fake news websites during the 2010 US midterm election, long before these phenomena became widely known in 2016. We develop public, state-of-the art network and data science methods and tools, such as Botometer, Hoaxy, and BotSlayer, to help researchers, journalists, and civil society organizations study coordinated inauthentic campaigns. So when Stanley-Becker contacted us about accounts posting identical political content on Twitter, we were happy to apply our analytical framework to map out what was going on.
Turning Point Action, Trolling, social media, Twitter
Twitter Data
Turning Point Action, Trolling, social media, Twitter
Twitter Data
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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