Taming TikTok: how BIPOC individuals perceive and interact with algorithmically generated content
Introduction. TikTok’s recommendation algorithm plays a crucial role in shaping user experiences, raising concerns about algorithmic bias, content suppression, and misinformation, particularly for BIPOC users. This study explores how BIPOC individuals perceive and interact with TikTok’s algorithm,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Borås
2025-03-01
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| Series: | Information Research: An International Electronic Journal |
| Subjects: | |
| Online Access: | https://publicera.kb.se/ir/article/view/47089 |
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| Summary: | Introduction. TikTok’s recommendation algorithm plays a crucial role in shaping user experiences, raising concerns about algorithmic bias, content suppression, and misinformation, particularly for BIPOC users. This study explores how BIPOC individuals perceive and interact with TikTok’s algorithm, focusing on content visibility, algorithmic manipulation, and experiences with problematic content.
Method. This pilot study utilized semi-structured interviews with 10 BIPOC TikTok users, recruited via social media. Participants discussed their experiences with the platform’s recommendation system, and responses were transcribed and thematically analyzed to identify patterns in content exposure, engagement strategies, and perceptions of algorithmic bias.
Analysis. A qualitative thematic coding approach was applied to categorize responses based on key themes, including content representation, misinformation, and strategies for mitigating algorithmic harm.
Results. Findings reveal that BIPOC users frequently encounter discriminatory content, misinformation, and algorithmic suppression. Many employ strategies such as selective engagement, the "Not Interested" feature, and algorithm resets to curate their feed. However, skepticism remains regarding TikTok’s data practices and fairness in content moderation.
Conclusions. BIPOC users actively shape their digital experiences to counteract algorithmic bias, highlighting systemic inequities in AI-driven platforms. Future research should expand participant diversity and investigate long-term algorithmic trends to promote inclusive digital spaces.
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| ISSN: | 1368-1613 |