论文标题
不根据用户的封面来判断：了解社交媒体研究中多模式处理的危害
Not Judging a User by Their Cover: Understanding Harm in Multi-Modal Processing within Social Media Research
论文作者
论文摘要
社交媒体震撼了我们社会的基础，这看起来似乎不太可能。但是，用于缓和有害数字内容的许多流行工具都受到了学术界和公共领域的广泛批评，涉及中等表现和缺乏问责制。尽管社交媒体研究被认为主要集中在自然语言处理上，但我们证明了社区了解多媒体处理及其独特的道德考虑因素。具体而言，当提供不同的信息方式时，我们确定了亚马逊土耳其人（MTURK）注释的性能的统计差异，并讨论众包人口预测产生的伤害模式。最后，我们通过审核在各种人口类别的Twitter用户的语言上审核毒性检测器的性能，讨论这些偏见的后果。
Social media has shaken the foundations of our society, unlikely as it may seem. Many of the popular tools used to moderate harmful digital content, however, have received widespread criticism from both the academic community and the public sphere for middling performance and lack of accountability. Though social media research is thought to center primarily on natural language processing, we demonstrate the need for the community to understand multimedia processing and its unique ethical considerations. Specifically, we identify statistical differences in the performance of Amazon Turk (MTurk) annotators when different modalities of information are provided and discuss the patterns of harm that arise from crowd-sourced human demographic prediction. Finally, we discuss the consequences of those biases through auditing the performance of a toxicity detector called Perspective API on the language of Twitter users across a variety of demographic categories.
