Doctoral Dissertation Research in Economics: Algorithmic Bias and Dynamics of Hate Speech on Social Media

该奖项支持对社交媒体算法的研究，这些算法会导致这些平台上的仇恨言论和两极分化。据认为，全球仇恨言论急剧增加的部分原因是社交媒体算法放大了这种仇恨言论。然而，由于缺乏适当的数据，研究人员无法测试情况是否如此。与世界上最大的社交媒体平台之一合作，研究人员将研究算法推荐对仇恨言论增加的影响，以及算法提示的过去接触仇恨内容对当前用户参与此类社交媒体帖子的累积影响。实验方法的使用将使研究人员能够将用户对仇恨言论的偏好的影响与仇恨言论的算法放大的影响分开。这项研究的结果将为减少社交媒体平台上的仇恨言论的政策提供重要的投入，从而使美国成为减少社交媒体上仇恨言论的全球领导者。在社交媒体平台上，算法推荐被广泛用于根据用户的偏好定制内容，导致一些信息被放大，但人们对这些算法对仇恨言论的因果影响知之甚少。这些算法根据不同用户对社交内容的天生偏好，向他们展示特定类型的内容。这些偏好不是由研究人员观察到的，而是由算法随着时间的推移而学习的。这个项目调查了算法推荐系统对放大仇恨言论参与的影响。为了实现这一目标，研究人员将与世界上最大的社交媒体平台之一合作进行大规模的随机对照试验。在这个实验中，将对一组随机的用户关闭内容推荐。因此，大量用户将接触到从整个帖子语料库中随机选择的内容。研究人员假设，过去的曝光对当前内容分享的影响将导致算法定制比没有这种动态影响时更加两极分化。这项研究的结果将为减少社交媒体平台上的仇恨言论的政策提供重要的投入，从而使美国成为减少社交媒体上仇恨言论的全球领导者。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award supports research on social media algorithms that lead to hate speech and polarization on these platforms. It is thought that the sharp increase in hate speech around the world is partly caused by social media algorithms that amplifies such hate speech. However, researchers have not been able to test whether this is the case or not for lack of appropriate data. Working with one of the largest social media platform in the world, the researchers will study the effects of algorithmic recommendations on increased hate speech and the cumulative effects of past exposure to hateful content prompted by algorithms on current user engagement with such social media posts. The use of experimental methods will allow the researchers to disentangle the effects of user preferences for hate speech from the effects of algorithmic amplification of hate speech. The results of this research will provide important inputs into policies to reduce hateful speech on social media platforms and thus establish the US as a global leader in reducing hate speech on social media. Algorithmic recommendations are widely used to tailor content to users’ preferences on social media platforms leading to amplification of some messages, yet little is known about the causal effect of these algorithms on hateful speech. The algorithms expose different users to specific kinds of content based on their innate preferences over social content. These preferences are not observed by the researcher but are learned by the algorithm over time. This project investigates the influence of algorithmic recommendation systems on the amplification of engagement with hate speech. To accomplish this, the researchers will conduct a large-scale RCT in collaboration with one of the largest social media platforms in the world. In this experiment, content recommendations will be switched off for a random set of users. As a result, a large number of users will be exposed to content that is chosen randomly from the entire corpus of posts. The researchers hypothesize that the effect of past exposure on sharing of current content will cause algorithmic customization to be more polarizing than it would be in the absence of such dynamic effects. The results of this research will provide important inputs into policies to reduce hateful speech on social media platforms and thus establish the US as a global leader in reducing hate speech on social media.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.