Content moderation is an essential part of online community health and governance. While much of extant research is centered on what happens to the content, moderation also involves the management of violators. This study focuses on how moderators (mods) make decisions about their actions after the violation takes place but before the sanction by examining how they "profile" the violators. Through observations and interviews with volunteer mods on Twitch, we found that mods engage in a complex process of collaborative evidence collection and profile violators into different categories to decide the type and extent of punishment. Mods consider violators' characteristics as well as behavioral history and violation context before taking moderation action. The main purpose of the profiling was to avoid excessive punishment and aim to integrate violators more into the community. We discuss the contributions of profiling to moderation practice and suggest design mechanisms to facilitate mods' profiling processes.
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Coordination and Collaboration: How do Volunteer Moderators Work as a Team in Live Streaming Communities?
Volunteer moderators (mods) play significant roles in developing moderation standards and dealing with harmful content in their micro-communities. However, little work explores how volunteer mods work as a team. In line with prior work about understanding volunteer moderation, we interview 40 volunteer mods on Twitch — a leading live streaming platform. We identify how mods collaborate on tasks (off-streaming coordination and preparation, in-stream real-time collaboration, and relationship building both off-stream and in-stream to reinforce collaboration) and how mods contribute to moderation standards (collaboratively working on the community rulebook and individually shaping community norms). We uncover how volunteer mods work as an effective team. We also discuss how the affordances of multi-modal communication and informality of volunteer moderation contribute to task collaboration, standards development, and mod’s roles and responsibilities.
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- Award ID(s):
- 1928627
- NSF-PAR ID:
- 10383981
- Date Published:
- Journal Name:
- CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 1 to 14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Modern social media platforms like Twitch, YouTube, etc., embody an open space for content creation and consumption. However, an unintended consequence of such content democratization is the proliferation of toxicity and abuse that content creators get subjected to. Commercial and volunteer content moderators play an indispensable role in identifying bad actors and minimizing the scale and degree of harmful content. Moderation tasks are often laborious, complex, and even if semi-automated, they involve high-consequence human decisions that affect the safety and popular perception of the platforms. In this paper, through an interdisciplinary collaboration among researchers from social science, human-computer interaction, and visualization, we present a systematic understanding of how visual analytics can help in human-in-the-loop content moderation. We contribute a characterization of the data-driven problems and needs for proactive moderation and present a mapping between the needs and visual analytic tasks through a task abstraction framework. We discuss how the task abstraction framework can be used for transparent moderation, design interventions for moderators’ well-being, and ultimately, for creating futuristic human-machine interfaces for data-driven content moderation.more » « less
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Content moderation is a crucial aspect of online platforms, and it requires human moderators (mods) to repeatedly review and remove harmful content. However, this moderation process can lead to cognitive overload and emotional labor for the mods. As new platforms and designs emerge, such as live streaming space, new challenges arise due to the real-time nature of the interactions. In this study, we examined the use of ignoring as a moderation strategy by interviewing 19 Twitch mods. Our findings indicated that ignoring involves complex cognitive processes and significant invisible labor in the decision-making process. Additionally, we found that ignoring is an essential component of real-time moderation. These preliminary findings suggest that ignoring has the potential to be a valuable moderation strategy in future interactive systems, which highlights the need to design better support for ignoring in interactive live-streaming systems.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3491102.3517628
