How You Ask Shapes What You Get: Auditing Breast-Cancer Misinformation in TikTok Search

2026-07-14

AI Summary

This study investigates how variations in user search queries on TikTok influence exposure to health-related misinformation about breast cancer. Through a controlled audit experiment using 30 newly created accounts and six distinct query types, the research systematically analyzes 9,020 search results, focusing for the first time on search queries rather than recommendation algorithms. Findings reveal that query phrasing significantly affects the likelihood of encountering misinformation, which is widely distributed across entire result pages. Queries related to alternative medicine yield misinformation rates of 54.1% in symptom identification and 53.5% in treatment contexts—7.6 to 8.6 times higher than those for medical queries, which still contain 6.3%–7.1% misinformation. This work underscores the critical role of search framing in shaping the quality of health information users access.

Abstract

Millions of people use TikTok to seek health information, yet little is known about how users' search queries shape exposure to health misinformation. Whereas prior algorithm audits have focused primarily on recommendation feeds, we examine TikTok's search system, where users explicitly express their information needs through query formulation. We conduct a controlled sock-puppet audit of TikTok Search using 30 fresh accounts assigned to six experimental conditions spanning three information-seeking framings (Medical Information, Alternative Medicine, and Peer Narrative) and two breast-cancer contexts (Symptom Noticing and Active Treatment). Across 9,020 usable search-result exposures, annotated using a validated vision-language model pipeline, we find that query framing is strongly associated with misinformation exposure. Alternative Medicine queries returned misinformation in 54.1\% of cancer-relevant results within the Symptom Noticing context and 53.5\% within the Active Treatment context, 8.6 times and 7.6 times higher, respectively, than clinically framed Medical Information queries. Even Medical Information queries returned measurable levels of possible misinformation (6.3\%--7.1\%), suggesting that explicit medical intent alone does not eliminate exposure. Moreover, for Alternative Medicine queries, possible misinformation appeared throughout the ranked search results rather than only near the top, showing that exposure is not confined to the highest-ranked results. Videos labeled as misinformation were also substantially more likely to contain comments promoting unsupported treatments or anti-standard-care views. These findings demonstrate that search query framing plays a central role in shaping misinformation exposure on TikTok and highlight the importance of auditing query-driven search systems alongside recommendation algorithms.

Problem

• misinformation
• TikTok search
• query framing
• breast cancer
• health information

Innovation

• algorithmic audit
• search query framing
• health misinformation
• TikTok
• vision-language model