AppealMod: Inducing Friction to Reduce Moderator Workload of Handling User Appeals

Author:

Atreja Shubham1ORCID,Im Jane2ORCID,Resnick Paul1ORCID,Hemphill Libby3ORCID

Affiliation:

1. School of Information, University of Michigan, Ann Arbor, MI, USA

2. School of Information & Division of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, USA

3. School of Information, University of Michigan & ICPSR, Ann Arbor, MI, USA

Abstract

As content moderation becomes a central aspect of all social media platforms and online communities, interest has grown in how to make moderation decisions contestable. On social media platforms where individual communities moderate their own activities, the responsibility to address user appeals falls on volunteers from within the community. While there is a growing body of work devoted to understanding and supporting the volunteer moderators' workload, little is known about their practice of handling user appeals. Through a collaborative and iterative design process with Reddit moderators, we found that moderators spend considerable effort in investigating user ban appeals and desired to directly engage with users and retain their agency over each decision. To fulfill their needs, we designed and built AppealMod, a system that induces friction in the appeals process by asking users to provide additional information before their appeals are reviewed by human moderators. In addition to giving moderators more information, we expected the friction in the appeal process would lead to a selection effect among users, with many insincere and toxic appeals being abandoned before getting any attention from human moderators. To evaluate our system, we conducted a randomized field experiment in a Reddit community of over 29 million users that lasted for four months. As a result of the selection effect, moderators viewed only 30% of initial appeals and less than 10% of the toxically worded appeals; yet they granted roughly the same number of appeals when compared with the control group. Overall, our system is effective at reducing moderator workload and minimizing their exposure to toxic content while honoring their preference for direct engagement and agency in appeals.

Funder

National science foundation

Publisher

Association for Computing Machinery (ACM)

Reference61 articles.

1. Human intervention in automated decision-making

2. Edgar Alvarez. 2019. Instagram will soon let you appeal post takedowns | Engadget -- engadget.com. https://www.engadget.com/2019-05-07-instagram-appeals-content-review-taken-down-posts.html. [Accessed 06-Jan-2023].

3. Classification and Its Consequences for Online Harassment

4. Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making

5. Categorizing Live Streaming Moderation Tools

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3