Evidence of a coordinated network amplifying inauthentic narratives in the 2020 election
<p>On 15 September 2020, the Washington Post published an article by Isaac Stanley-Becker titled “<a href="https://www.washingtonpost.com/politics/turning-point-teens-disinformation-trump/2020/09/15/c84091ae-f20a-11ea-b796-2dd09962649c_story.html">Pro-Trump youth group enlists teens in secretive campaign likened to a ‘troll farm,’ prompting rebuke by Facebook and Twitter</a>.” 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.</p> <p>Our Observatory on Social Media at Indiana University has been studying <a href="https://theconversation.com/misinformation-on-social-media-can-technology-save-us-69264">social media manipulation</a> and <a href="https://theconversation.com/misinformation-and-biases-infect-social-media-both-intentionally-and-accidentally-97148">online misinformation</a> for over ten years. We uncovered the first known instances of <a href="http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2850">astroturf campaigns</a>, <a href="https://cacm.acm.org/magazines/2016/7/204021-the-rise-of-social-bots/fulltext">social bots</a>, and <a href="http://doi.org/10.1126/science.aao2998">fake news</a> 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 <a href="https://osome.iu.edu/tools/">tools</a>, such as <a href="https://botometer.osome.iu.edu/">Botometer</a>, <a href="https://hoaxy.iuni.iu.edu/">Hoaxy</a>, and <a href="https://osome.iu.edu/tools/botslayer/">BotSlayer</a>, 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 <a href="https://arxiv.org/abs/2001.05658">analytical framework</a> to map out what was going on. </p>
ShareScore
40/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 4