The spread of low-credibility content by social bots

• Published in: Nature communications Vol. 9; no. 1; pp. 4787 - 9
• Main Authors: Shao, Chengcheng, Ciampaglia, Giovanni Luca, Varol, Onur, Yang, Kai-Cheng, Flammini, Alessandro, Menczer, Filippo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.11.2018; Nature Publishing Group; Nature Portfolio
• Subjects: 639/766/530/2801; 706/689/454; 706/689/522; 706/703/559; Cognitive ability; False information; Humanities and Social Sciences; Information dissemination; multidisciplinary; Science; Science (multidisciplinary); Software agents
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-018-06930-7

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. Online misinformation is a threat to a well-informed electorate and undermines democracy. Here, the authors analyse the spread of articles on Twitter, find that bots play a major role in the spread of low-credibility content and suggest control measures for limiting the spread of misinformation.