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社交机器人传播低可信度内容。

The spread of low-credibility content by social bots.

机构信息

School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, 47408, IN, USA.

College of Computer, National University of Defense Technology, Changsha, 410073, Hunan, China.

出版信息

Nat Commun. 2018 Nov 20;9(1):4787. doi: 10.1038/s41467-018-06930-7.

Abstract

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.

摘要

大规模传播的数字错误信息已被确定为民主的主要威胁。通信、认知、社会和计算机科学家正在研究错误信息病毒式扩散的复杂原因,而在线平台也开始部署对策。几乎没有系统的、基于数据的证据来指导这些努力。在这里,我们分析了 2016 年至 2017 年十个月期间在 Twitter 上传播的 1400 万条信息和 40 万篇文章。我们有证据表明,社交机器人在传播低可信度来源的文章方面发挥了不成比例的作用。机器人在文章传播的早期就放大了这类内容,使其在病毒式传播之前就开始传播。它们还通过回复和提及来针对有很多关注者的用户。人类容易受到这种操纵,重新分享机器人发布的内容。成功的低可信度来源得到了社交机器人的大力支持。这些结果表明,遏制社交机器人可能是减轻在线错误信息传播的有效策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8a4/6246561/8d17bfc908e3/41467_2018_6930_Fig1_HTML.jpg

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