The spread of low-credibility content by social bots
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2017
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| _version_ | 1866909047743053824 |
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| author | Shao, Chengcheng Ciampaglia, Giovanni Luca Varol, Onur Yang, Kaicheng Flammini, Alessandro Menczer, Filippo |
| author_facet | Shao, Chengcheng Ciampaglia, Giovanni Luca Varol, Onur Yang, Kaicheng Flammini, Alessandro Menczer, Filippo |
| contents | The massive spread of digital misinformation has been identified as a major global risk and has been alleged to influence elections and threaten democracies. Communication, cognitive, social, and computer scientists are engaged in efforts to study the complex causes for the viral diffusion of misinformation online and to develop solutions, while search and social media platforms are beginning to deploy countermeasures. With few exceptions, these efforts have been mainly informed by anecdotal evidence rather than systematic data. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during and following the 2016 U.S. presidential campaign and election. We find evidence that social bots played a disproportionate role in amplifying low-credibility content. Accounts that actively spread articles from low-credibility sources are significantly more likely to be bots. Automated accounts are particularly active in amplifying content in the very early spreading moments, before an article goes viral. Bots also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, retweeting bots who post links to low-credibility content. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1707_07592 |
| institution | arXiv |
| publishDate | 2017 |
| record_format | arxiv |
| spellingShingle | The spread of low-credibility content by social bots Shao, Chengcheng Ciampaglia, Giovanni Luca Varol, Onur Yang, Kaicheng Flammini, Alessandro Menczer, Filippo Social and Information Networks Computers and Society Physics and Society The massive spread of digital misinformation has been identified as a major global risk and has been alleged to influence elections and threaten democracies. Communication, cognitive, social, and computer scientists are engaged in efforts to study the complex causes for the viral diffusion of misinformation online and to develop solutions, while search and social media platforms are beginning to deploy countermeasures. With few exceptions, these efforts have been mainly informed by anecdotal evidence rather than systematic data. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during and following the 2016 U.S. presidential campaign and election. We find evidence that social bots played a disproportionate role in amplifying low-credibility content. Accounts that actively spread articles from low-credibility sources are significantly more likely to be bots. Automated accounts are particularly active in amplifying content in the very early spreading moments, before an article goes viral. Bots also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, retweeting bots who post links to low-credibility content. 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. |
| title | The spread of low-credibility content by social bots |
| topic | Social and Information Networks Computers and Society Physics and Society |
| url | https://arxiv.org/abs/1707.07592 |