Who moderates the moderators?

Abstract

A large fraction of user-generated content on the Web, such as posts or comments on popular online forums, consists of abuse or spam. Due to the volume of contributions on popular sites, a few trusted moderators cannot identify all such abusive content, so viewer ratings of contributions must be used for moderation. But not all viewers who rate content are trustworthy and accurate. What is a principled approach to assigning trust and aggregating user ratings, in order to accurately identify abusive content?In this paper, we introduce a framework to address the problem of moderating online content using crowdsourced ratings. Our framework encompasses users who are untrustworthy or inaccurate to an unknown extent -that is, both the content and the raters are of unknown quality. With no knowledge whatsoever about the raters, it is impossible to do better than a random estimate. We present efficient algorithms to accurately detect abuse that only require knowledge about the identity of a single 'good' agent, who rates contributions accurately more than half the time. We prove that our algorithm can infer the quality of contributions with error that rapidly converges to zero as the number of observations increases; we also numerically demonstrate that the algorithm has very high accuracy for much fewer observations. Finally, we analyze the robustness of our algorithms to manipulation by adversarial or strategic raters, an important issue in moderating online content, and quantify how the performance of the algorithm degrades with the number of manipulating agents.

Crowdboard

“…Nevertheless moderator's intervention was sometimes needed when off-topic discussions on individual technical issues were causing too much distraction from the main task. Future iterations of the system may include different crowd roles, including moderators, and/or features for self-moderation [14].…”

Training Complex Models with Multi-Task Weak Supervision

“…Our approach also has connections to the crowdsourcing literature [17; 11], and in particular to spectral and method of moments-based approaches [38; 9; 12; 1]. In contrast, the goal of our work is to support and explore settings not covered by crowdsourcing work, such as sources with correlated outputs, the proposed multi-task supervision setting, and regimes wherein a small number of labelers (weak supervision sources) each label a large number of items (data points).…”

Reconsidering Self-Moderation

“…Geiger studied one collective approach to mutual moderation via Twitter blocklists, where users work together to coordinate lists of users who they all agree to block [33], a phenomenon further explored in research by Jhaver et al [51], though in a sense this is more a form of community self-moderation than platform-driven moderation. Other work has proposed methods for making user reports more effective; Ghosh, Kale, and McAfee [38] proposed a computational approach for identifying trustworthy volunteer content raters and screening out bad actors, an approach that translates well to, e.g., identifying trustworthy reports on Reddit.…”