Paper Type
Complete
Paper Number
1715
Description
Collective anonymity is defined as a large group of social media users collectively adopting a uniform identification presentation (e.g., an identical online pseudonym and avatar). This emerging trend is increasingly prevalent on algorithm-driven social media, proactively leveraged by users to increase perceived anonymity. To conceptualize it and understand its drivers and outcomes, this study investigated one exemplary form of collective anonymity on Xiaohongshu. Using an inductive approach, interview data with fourteen participants was collected and analysed through a thematic approach. Our findings (a) explained the underlying mechanisms of collective anonymity; (b) unpacked users’ internal motivations and extrinsic factors that drive it; (c) uncovered its downstream consequences pertinent to human-human interaction and human-algorithm engagement. Our study also provides important implications on algorithmic regulation and governance, the ethical use of algorithmic recommendations, and the mitigation of disinhibited and deviant behaviour resulting from collective anonymity.
Recommended Citation
Li, Qianyi; Zhang, Mingxin; Tan, Dilang; Liu, Libo; and Zhang, Yixin, "“Hiding in the Crowd?” In Pursuit of Perceived Anonymity through a Uniform Visual Presentation on Algorithm-driven Social Media Platforms" (2024). PACIS 2024 Proceedings. 2.
https://aisel.aisnet.org/pacis2024/track13_hcinteract/track13_hcinteract/2
“Hiding in the Crowd?” In Pursuit of Perceived Anonymity through a Uniform Visual Presentation on Algorithm-driven Social Media Platforms
Collective anonymity is defined as a large group of social media users collectively adopting a uniform identification presentation (e.g., an identical online pseudonym and avatar). This emerging trend is increasingly prevalent on algorithm-driven social media, proactively leveraged by users to increase perceived anonymity. To conceptualize it and understand its drivers and outcomes, this study investigated one exemplary form of collective anonymity on Xiaohongshu. Using an inductive approach, interview data with fourteen participants was collected and analysed through a thematic approach. Our findings (a) explained the underlying mechanisms of collective anonymity; (b) unpacked users’ internal motivations and extrinsic factors that drive it; (c) uncovered its downstream consequences pertinent to human-human interaction and human-algorithm engagement. Our study also provides important implications on algorithmic regulation and governance, the ethical use of algorithmic recommendations, and the mitigation of disinhibited and deviant behaviour resulting from collective anonymity.
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