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

经济学博士论文研究：算法偏差和社交媒体上仇恨言论的动态

• 批准号: 2315380
• 负责人: Andrew Foster
• 金额: $ 2.05万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2315380&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Economics; Algorithmic

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.

该奖项支持对导致这些平台上的仇恨言论和两极分化的社交媒体算法的研究。人们认为，世界各地仇恨言论的急剧增加部分是由放大此类仇恨言论的社交媒体算法造成的。但研究人员尚未对此进行研究。由于缺乏适当的数据，研究人员将与世界上最大的社交媒体平台之一合作，研究算法建议对仇恨言论增加的影响以及过去接触仇恨言论的累积影响。算法提示当前用户参与此类活动的仇恨内容社交媒体帖子的使用将使研究人员能够将仇恨言论的用户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仇恨言论的政策提供重要的投入。算法推荐被广泛用于根据社交媒体平台上用户的偏好定制内容，导致某些信息被放大，但人们对其因果影响知之甚少。这些算法对可恶的这些算法根据不同的用户对社交内容的固有偏好来接触特定类型的内容，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。该项目研究了算法推荐系统对放大的影响。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随机对照试验，在该实验中，将为随机一组用户关闭内容推荐。这样一来，就会有大量的用户被曝光研究人员认为，与没有这种动态效果的情况相比，过去的曝光对当前内容共享的影响将导致算法定制更加两极分化。这项研究将为减少社交媒体平台上的仇恨言论的政策提供重要投入，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 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准。