"I Searched for a Religious Song in Amharic and Got Sexual Content Instead": Investigating Online Harm in Low-Resourced Languages on YouTube
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Abstract (arXiv)
Online social media platforms such as YouTube have a wide, global reach. However, little is known about the experience of low-resourced language speakers on such platforms; especially in how they experience and navigate harmful content. To better understand this, we (1) conducted semi-structured interviews (n=15) and (2) analyzed search results (n=9313), recommendations (n=3336), channels (n=120) and comments (n=406) of policy-violating sexual content on YouTube focusing on the Amharic language. Our findings reveal that -- although Amharic-speaking YouTube users find the platform crucial for several aspects of their lives -- participants reported unplanned exposure to policy-violating sexual content when searching for benign, popular queries. Furthermore, malicious content creators seem to exploit under-performing language technologies and content moderation to further target vulnerable groups of speakers, including migrant domestic workers, diaspora, and local Ethiopians. Overall, our study sheds light on how failures in low-resourced language technology may lead to exposure to harmful content and suggests implications for stakeholders in minimizing harm. Content Warning: This paper includes discussions of NSFW topics and harmful content (hate, abuse, sexual harassment, self-harm, misinformation). The authors do not support the creation or distribution of harmful content.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 May 2024.
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Citing papers per year: 2024: 4; 2025: 5.
9 papers.
- 2024Consent in Crisis: The Rapid Decline of the AI Data Commonscited by 45
- 2025The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating Itcited by 42
- 2024Preference Tuning For Toxicity Mitigation Generalizes Across Languagescited by 31
- 2024The Zeno's Paradox of `Low-Resource' Languagescited by 12
- 2025LLM-C3MOD: A Human-LLM Collaborative System for Cross-Cultural Hate Speech Moderationcited by 5
- 2025Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrievalcited by 3
- 2025NLP Security and Ethics, in the Wildcited by 2
- 2024A Capabilities Approach to Studying Bias and Harm in Language Technologiescited by 0
- 2025Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activitiescited by 0
- 2025The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It30 May 2025
- 2025Optimized Text Embedding Models and Benchmarks for Amharic Passage Retrieval25 May 2025
- 2025NLP Security and Ethics, in the Wild9 Apr 2025
- 2025Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities8 Apr 2025
- 2025LLM-C3MOD: A Human-LLM Collaborative System for Cross-Cultural Hate Speech Moderation10 Mar 2025
- 2024A Capabilities Approach to Studying Bias and Harm in Language Technologies6 Nov 2024
- 2024The Zeno's Paradox of `Low-Resource' Languages28 Oct 2024
- 2024Consent in Crisis: The Rapid Decline of the AI Data Commons20 Jul 2024
- 2024Preference Tuning For Toxicity Mitigation Generalizes Across Languages23 Jun 2024