skip to main content


Title: Wisdom of Two Crowds: Misinformation Moderation on Reddit and How to Improve this Process---A Case Study of COVID-19
Past work has explored various ways for online platforms to leverage crowd wisdom for misinformation detection and moderation. Yet, platforms often relegate governance to their communities, and limited research has been done from the perspective of these communities and their moderators. How is misinformation currently moderated in online communities that are heavily self-governed? What role does the crowd play in this process, and how can this process be improved? In this study, we answer these questions through semi-structured interviews with Reddit moderators. We focus on a case study of COVID-19 misinformation. First, our analysis identifies a general moderation workflow model encompassing various processes participants use for handling COVID-19 misinformation. Further, we show that the moderation workflow revolves around three elements: content facticity, user intent, and perceived harm. Next, our interviews reveal that Reddit moderators rely on two types of crowd wisdom for misinformation detection. Almost all participants are heavily reliant on reports from crowds of ordinary users to identify potential misinformation. A second crowd--participants' own moderation teams and expert moderators of other communities--provide support when participants encounter difficult, ambiguous cases. Finally, we use design probes to better understand how different types of crowd signals---from ordinary users and moderators---readily available on Reddit can assist moderators with identifying misinformation. We observe that nearly half of all participants preferred these cues over labels from expert fact-checkers because these cues can help them discern user intent. Additionally, a quarter of the participants distrust professional fact-checkers, raising important concerns about misinformation moderation.  more » « less
Award ID(s):
2045432
NSF-PAR ID:
10427126
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the ACM on Human-Computer Interaction
Volume:
7
Issue:
CSCW1
ISSN:
2573-0142
Page Range / eLocation ID:
1 to 33
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Online volunteers are an uncompensated yet valuable labor force for many social platforms. For example, volunteer content moderators perform a vast amount of labor to maintain online communities. However, as social platforms like Reddit favor revenue generation and user engagement, moderators are under-supported to manage the expansion of online communities. To preserve these online communities, developers and researchers of social platforms must account for and support as much of this labor as possible. In this paper, we quantitatively characterize the publicly visible and invisible actions taken by moderators on Reddit, using a unique dataset of private moderator logs for 126 subreddits and over 900 moderators. Our analysis of this dataset reveals the heterogeneity of moderation work across both communities and moderators. Moreover, we find that analyzing only visible work – the dominant way that moderation work has been studied thus far – drastically underestimates the amount of human moderation labor on a subreddit. We discuss the implications of our results on content moderation research and social platforms. 
    more » « less
  2. null (Ed.)
    Volunteer moderators actively engage in online content management, such as removing toxic content and sanctioning anti-normative behaviors in user-governed communities. The synchronicity and ephemerality of live-streaming communities pose unique moderation challenges. Based on interviews with 21 volunteer moderators on Twitch, we mapped out 13 moderation strategies and presented them in relation to the bad act, enabling us to categorize from proactive and reactive perspectives and identify communicative and technical interventions. We found that the act of moderation involves highly visible and performative activities in the chat and invisible activities involving coordination and sanction. The juxtaposition of real-time individual decision-making with collaborative discussions and the dual nature of visible and invisible activities of moderators provide a unique lens into a role that relies heavily on both the social and technical. We also discuss how the affordances of live-streaming contribute to these unique activities. 
    more » « less
  3. null (Ed.)
    Content moderation is a critical service performed by a variety of people on social media, protecting users from offensive or harmful content by reviewing and removing either the content or the perpetrator. These moderators fall into one of two categories: employees or volunteers. Prior research has suggested that there are differences in the effectiveness of these two types of moderators, with the more transparent user-based moderation being useful for educating users. However, direct comparisons between commercially-moderated and user-moderated platforms are rare, and apart from the difference in transparency, we still know little about what other disparities in user experience these two moderator types may create. To explore this, we conducted cross-platform surveys of over 900 users of commercially-moderated (Facebook, Instagram, Twitter, and YouTube) and user-moderated (Reddit and Twitch) social media platforms. Our results indicated that although user-moderated platforms did seem to be more transparent than commercially-moderated ones, this did not lead to user-moderated platforms being perceived as less toxic. In addition, commercially-moderated platform users want companies to take more responsibility for content moderation than they currently do, while user-moderated platform users want designated moderators and those who post on the site to take more responsibility. Across platforms, users seem to feel powerless and want to be taken care of when it comes to content moderation as opposed to engaging themselves. 
    more » « less
  4. To address the widespread problem of uncivil behavior, many online discussion platforms employ human moderators to take action against objectionable content, such as removing it or placing sanctions on its authors. Thisreactive paradigm of taking action against already-posted antisocial content is currently the most common form of moderation, and has accordingly underpinned many recent efforts at introducing automation into the moderation process. Comparatively less work has been done to understand other moderation paradigms---such as proactively discouraging the emergence of antisocial behavior rather than reacting to it---and the role algorithmic support can play in these paradigms. In this work, we investigate such a proactive framework for moderation in a case study of a collaborative setting: Wikipedia Talk Pages. We employ a mixed methods approach, combining qualitative and design components for a holistic analysis. Through interviews with moderators, we find that despite a lack of technical and social support, moderators already engage in a number of proactive moderation behaviors, such as preemptively intervening in conversations to keep them on track. Further, we explore how automation could assist with this existing proactive moderation workflow by building a prototype tool, presenting it to moderators, and examining how the assistance it provides might fit into their workflow. The resulting feedback uncovers both strengths and drawbacks of the prototype tool and suggests concrete steps towards further developing such assisting technology so it can most effectively support moderators in their existing proactive moderation workflow. 
    more » « less
  5. Abstract

    We show that malicious COVID-19 content, including racism, disinformation, and misinformation, exploits the multiverse of online hate to spread quickly beyond the control of any individual social media platform. We provide a first mapping of the online hate network across six major social media platforms. We demonstrate how malicious content can travel across this network in ways that subvert platform moderation efforts. Machine learning topic analysis shows quantitatively how online hate communities are sharpening COVID-19 as a weapon, with topics evolving rapidly and content becoming increasingly coherent. Based on mathematical modeling, we provide predictions of how changes to content moderation policies can slow the spread of malicious content.

     
    more » « less