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Abstract Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter’s news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.
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Can Social Media Anti-abuse Policies Work? A Quasi-experimental Study of Online Sexist and Racist Slurs
The authors use the timing of a change in Twitter’s rules regarding abusive content to test the effectiveness of organizational policies aimed at stemming online harassment. Institutionalist theories of social control suggest that such interventions can be efficacious if they are perceived as legitimate, whereas theories of psychological reactance suggest that users may instead ratchet up aggressive behavior in response to the sanctioning authority. In a sample of 3.6 million tweets spanning one month before and one month after Twitter’s policy change, the authors find evidence of a modest positive shift in the average sentiment of tweets with slurs targeting women and/or African Americans. The authors further illustrate this trend by tracking the network spread of specific tweets and individual users. Retweeted messages are more negative than those not forwarded. These patterns suggest that organizational “anti-abuse” policies can play a role in stemming hateful speech on social media without inflaming further abuse.
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- Award ID(s):
- 1818497
- PAR ID:
- 10547554
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Socius: Sociological Research for a Dynamic World
- Volume:
- 6
- ISSN:
- 2378-0231
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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