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Auditing TikTok’s Recommender System with Sock Puppets

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Title: Auditing TikTok’s Recommender System with Sock Puppets
Author(s): Aarne, Onni
Contributor: University of Helsinki, Faculty of Science
Degree program: Master's Programme in Data Science
Specialisation: no specialization
Language: English
Acceptance year: 2022
The content we see is increasingly determined by ever more advanced recommender systems, and popular social media platform TikTok represents the forefront of this development (See Chapter 1). There has been much speculation about the workings of these recommender systems, but precious little systematic, controlled study (See Chapter 2). To improve our understanding of these systems, I developed sock puppet bots that consume content on TikTok as a normal user would (See Chapter 3). This allowed me to run controlled experiments to see how the TikTok recommender system would respond to sock puppets exhibiting different behaviors and preferences in a Finnish context, and how this would differ from the results obtained by earlier investigations (See Chapter 4). This research was done as part of a journalistic investigation in collaboration with Long Play. I found that TikTok appears to have adjusted their recommender system to personalize content seen by users to a much lesser degree, likely in response to a previous investigation by the WSJ. However, I came to the conclusion that, while sock puppet audits can be useful, they are not a sufficiently scalable solution to algorithm governance, and other types of audits with more internal access are needed (See Chapter 5).
Keyword(s): TikTok algorithm auditing algorithmic accountability recommender systems

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