Wang Yichen, Han Richard, Lehman Tamara Silbergleit, Lv Qin, Mishra Shivakant
Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309 USA.
Department of Electrical, Computer and Energy Engineering, University of Colorado Boulder, Boulder, CO 80309 USA.
Soc Netw Anal Min. 2022;12(1):167. doi: 10.1007/s13278-022-00992-8. Epub 2022 Nov 10.
Social media platforms have been exploited to disseminate misinformation in recent years. The widespread online misinformation has been shown to affect users' beliefs and is connected to social impact such as polarization. In this work, we focus on misinformation's impact on specific user behavior and aim to understand whether general Twitter users changed their behavior after being exposed to misinformation. We compare the before- and after-exposure behaviors of Twitter users to determine whether they changed their tweeting frequency, tweets sentiment, usage of specific types of words, and the ratio of liberal/conservative media URLs they shared. Our results show that users overall exhibited statistically significant changes in behavior across some of these metrics. Through language distance analysis, we show that exposed users were already different from baseline users before the exposure. We also study the characteristics of several specific user groups, which include liberal/conservative leaning groups and multi-exposure groups. Furthermore, we study whether the users' behavior changes after exposure to misinformation tweets vary based on their follower count or the follower count of the tweet authors. Finally, we examine potential bots' behaviors and find they are similar to that of normal users.
近年来,社交媒体平台被用于传播错误信息。事实证明,广泛传播的网络错误信息会影响用户的信念,并与诸如两极分化等社会影响相关联。在这项研究中,我们关注错误信息对特定用户行为的影响,旨在了解普通推特用户在接触到错误信息后是否改变了他们的行为。我们比较推特用户接触错误信息前后的行为,以确定他们是否改变了推文频率、推文情绪、特定类型词汇的使用以及他们分享的自由派/保守派媒体网址的比例。我们的结果表明,用户在这些指标中的一些指标上总体表现出具有统计学意义的行为变化。通过语言距离分析,我们表明,接触错误信息的用户在接触之前就已经与基线用户不同。我们还研究了几个特定用户群体的特征,其中包括倾向自由派/保守派的群体和多次接触错误信息的群体。此外,我们研究了用户在接触错误信息推文后行为的变化是否因他们的关注者数量或推文作者的关注者数量而有所不同。最后,我们检查了潜在机器人的行为,发现它们与普通用户的行为相似。