The spread of low-credibility content by social bots

Author: Shao, C.; Ciampaglia, G. L.; Varol, O.; Yang, K.-C.; Flammini, A. & Menczer, F.

Description: The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources. Bots amplify such content in the early spreading moments, before an article goes viral. They also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, resharing content posted by bots. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.

Subject headings: Communication; Data Collection, methods, statistics & numerical data; Humans; Information Dissemination, methods; Social Media, standards, statistics & numerical data; Social Networking; Misinformation; Disinformation

Publication year: 2018

Journal or book title: Nature communications

Volume: 9

Issue: 1

Pages: 4787

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Type: Journal article

Serial number: 3059

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