The year 2020 will be remembered for two events of global significance: the COVID-19 pandemic and 2020 U.S. Presidential Election. In this chapter, we summarize recent studies using large public Twitter data sets on these issues. We have three primary objectives. First, we delineate epistemological and practical considerations when combining the traditions of computational research and social science research. A sensible balance should be struck when the stakes are high between advancing social theory and concrete, timely reporting of ongoing events. We additionally comment on the computational challenges of gleaning insight from large amounts of social media data. Second, we characterize the role of social bots in social media manipulation around the discourse on the COVID-19 pandemic and 2020 U.S. Presidential Election. Third, we compare results from 2020 to prior years to note that, although bot accounts still contribute to the emergence of echo-chambers, there is a transition from state-sponsored campaigns to domestically emergent sources of distortion. Furthermore, issues of public health can be confounded by political orientation, especially from localized communities of actors who spread misinformation. We conclude that automation and social media manipulation pose issues to a healthy and democratic discourse, precisely because they distort representation of pluralism within the public sphere.

中文翻译：

---

2020年的社交机器人和社交媒体操纵：回顾年

2020年将因两个具有全球意义的事件而被铭记：COVID-19大流行和2020年美国总统大选。在本章中，我们总结了有关这些问题的大型公共Twitter数据集的最新研究。我们有三个主要目标。首先，在结合计算研究和社会科学研究的传统时，我们概述了认识论和实践考虑。当先进的社会理论与对正在进行的事件的具体及时报告之间的利害关系重大时，应取得明智的平衡。我们还评论了从大量社交媒体数据中收集见解的计算挑战。其次，围绕COVID-19大流行和2020年美国总统大选的讨论，我们描述社交机器人在社交媒体操纵中的作用。第三，我们将2020年与往年的结果进行比较，以注意到，尽管机器人帐户仍然为回声腔的出现做出了贡献，但从国家资助的运动到国内新兴的畸变源也有过渡。此外，公共卫生问题可能会因政治倾向而混淆，特别是来自散布错误信息的行为者本地化社区。我们得出的结论是，自动化和社交媒体操纵给健康，民主的话语带来了问题，这恰恰是因为它们扭曲了公共领域内多元化的代表。政治取向可能会混淆公共卫生问题，尤其是来自散布错误信息的行为者的本地社区。我们得出的结论是，自动化和社交媒体操纵给健康和民主的话语带来了问题，这恰恰是因为它们扭曲了公共领域内的多元化代表。政治取向可能会混淆公共卫生问题，特别是来自散布错误信息的行为者本地化社区。我们得出的结论是，自动化和社交媒体操纵给健康，民主的话语带来了问题，这恰恰是因为它们扭曲了公共领域内多元化的代表。