The spread of low-credibility content by social bots
Chengcheng Shao Indiana University, Bloomington Giovanni Luca Ciampaglia Indiana University, Bloomington Onur Varol Indiana University, Bloomington Kaicheng Yang Indiana University, Bloomington Alessandro Flammini Indiana University, Bloomington Filippo Menczer Indiana University, Bloomington
Abstract
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 co
中文速览
虚假信息在社交媒体上为何传播得如此之快？一个被忽视的关键推手是"社交机器人"（social bots）——这类由软件自动控制的账号。研究者利用 Hoaxy 平台和 Botometer 算法，系统分析了 2016 年美国大选前后 Twitter 上 1400 万条帖子传播约 40 万篇文章的完整数据，发现机器人账号虽然只占传播低可信度内容账号总数的 6%，却贡献了其中超过 30% 的推文，且它们会在文章刚发布的最初几秒钟抢先大量转发，并反复 @拥有大量粉丝的意见领袖，人为制造出"这条内容很受欢迎"的假象。更值得警惕的是，普通人根本无法分辨低可信度内容是机器人还是真人发出的，机器人每产生一定量的传播，就会带动人类用户超线性地跟进转发，形成显著的放大效应。这项研究首次以大规模数据量化了机器人在虚假信息扩散中的系统性作用，表明精准打击社交机器人或许是遏制网络谣言病毒式传播最直接有效的切入点。
原文 arXiv:1707.07592;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1707.07592v4