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.

该奖项支持有关社交媒体算法的研究，这些研究导致这些平台上的仇恨言论和两极分化。人们认为，世界各地的仇恨言论的急剧增长是由社交媒体算法引起的，这些算法放大了这种仇恨言论。但是，研究人员由于缺乏适当的数据而无法测试是否是这种情况。研究人员与世界上最大的社交媒体平台之一合作，将研究算法建议对增加仇恨言论的影响以及过去暴露于算法促使仇恨内容的累积影响对当前用户参与此类社交媒体帖子的影响。实验方法的使用将使研究人员能够将仇恨言论的用户对仇恨言论的影响从仇恨言论的算法放大的影响中解散。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意见，从而确立美国在减少社交媒体上仇恨言论的全球领导者。算法的建议被广泛用于将内容定制为在社交媒体平台上的偏好，从而扩大某些消息，但是对于这些算法对仇恨言论的灾难性影响知之甚少。这些算法会根据其与社交内容的先天偏好相比，将不同的用户暴露于特定的内容。这些偏好并未被研究人员观察到，而是通过算法随着时间的流逝而学到的。该项目调查了算法推荐系统对扩大与仇恨言论的互动的影响。为此，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RCT。在此实验中，将关闭一组随机用户的内容建议。结果，大量用户将暴露于从整个帖子语料库中随机选择的内容。研究人员假设过去暴露对当前内容共享的影响将导致算法定制比没有这种动态效应的情况更具两极分化。这项研究的结果将为减少社交媒体平台上的仇恨言论提供重要的投入，从而确立美国在社交媒体上减少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知识分子优点和更广泛的影响审查标准，通过评估来诚实地对支持进行评估。