Renkai Ma
ABSTRACT
My doctoral research aims to conceptualize and improve creator moderation design based on (1) content creators’ voice and (2) multiple platform stakeholders’ collaboration. Four of our previous publications in the CSCW conferences have uncovered the socioeconomic impacts of moderation on creators, as well as the fairness and bureaucracy challenges they undertake, implicating the transparency design requirements for addressing these challenges. Two of proposed future studies will pivot into evaluating and refining creator moderation design with different platform stakeholders. First, I will explore how audiences and creators recognize creator moderation's compatibility with platform ecosystem (e.g., monetization, recommendation algorithms). Second, I will organize participatory design workshops with stakeholders such as creators, commercial moderators, and audiences to redesign creator moderation to balance different stakeholders’ stakes and platform's top-down moderation efforts. My dissertation aims to contribute to HCI and CSCW fields through the notion of creator moderation and comprehensive design and policy solutions for a safe and sustaining platform ecosystem.
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- Jessica L. Feuston, Alex S. Taylor, and Anne Marie Piper. 2020. Conformity of Eating Disorders through Content Moderation. Proc ACM Hum Comput Interact 4, CSCW1 (May 2020). DOI:https://doi.org/10.1145/3392845Google ScholarDigital Library
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- Shagun Jhaver, Quan Ze Chen, Detlef Knauss, and Amy Zhang. 2022. Designing Word Filter Tools for Creator-led Comment Moderation. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (2022). DOI:https://doi.org/10.1145/3491102.3517505Google ScholarDigital Library
- Prerna Juneja, Deepika Rama Subramanian, and Tanushree Mitra. 2020. Through the looking glass: Study of transparency in Reddit's moderation practices. Proc ACM Hum Comput Interact 4, GROUP (January 2020), 1–35. DOI:https://doi.org/10.1145/3375197Google ScholarDigital Library
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- Renkai Ma and Yubo Kou. 2021. “How advertiser-friendly is my video?”: YouTuber's Socioeconomic Interactions with Algorithmic Content Moderation. PACM on Human Computer Interaction 5, CSCW2 (2021), 1–26. DOI:https://doi.org/https://doi.org/10.1145/3479573Google ScholarDigital Library
- Renkai Ma and Yubo Kou. 2022. “I'm not sure what difference is between their content and mine, other than the person itself”: A Study of Fairness Perception of Content Moderation on YouTube. Proc ACM Hum Comput Interact 6, CSCW2 (2022), 28. DOI:https://doi.org/10.1145/3555150Google ScholarDigital Library
- Renkai Ma and Yubo Kou. 2022. “I am not a YouTuber who can make whatever video I want. I have to keep appeasing algorithms”: Bureaucracy of Creator Moderation on YouTube. In Companion Computer Supported Co-operative Work and Social Computing (CSCW’22 Companion). Retrieved from https://doi.org/10.1145/3500868.3559445Google ScholarDigital Library
- Renkai Ma and Yubo Kou. 2023. “Defaulting to boilerplate answers, they didn't engage in a genuine conversation”: Dimensions of Transparency Design in Creator Moderation. Proc ACM Hum Comput Interact 7, CSCW1 (April 2023), 44. DOI:https://doi.org/10.1145/3579477Google ScholarDigital Library
- Kaitlin Mahar, Amy X Zhang, and David Karger. 2018. Squadbox: A Tool to Combat Email Harassment Using Friendsourced Moderation. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–13. Retrieved from https://doi.org/10.1145/3173574.3174160Google ScholarDigital Library
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- Michael Veale, Max Van Kleek, and Reuben Binns. 2018. Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (2018). DOI:https://doi.org/10.1145/3173574Google ScholarDigital Library
- Austin P. Wright, Omar Shaikh, Haekyu Park, Will Epperson, Muhammed Ahmed, Stephane Pinel, Duen Horng (Polo) Chau, and Diyi Yang. 2021. RECAST: Enabling User Recourse and Interpretability of Toxicity Detection Models with Interactive Visualization. Proc ACM Hum Comput Interact 5, CSCW1 (April 2021), 1–26. DOI:https://doi.org/10.1145/3449280Google ScholarDigital Library
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