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DOI 10.3760/cma.j.cn112338-20250626-00438

Медицинская библиотека · Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi · 2026

[Analysis on HIV/AIDS related topics of public concern on TikTok platform: based on latent dirichlet allocation model].

Zhang Y, Luo D H, Dong W, Xu J, Xu J F, Zhou C

Аннотация на английскомСтатьяПеревод в очереди

Аннотация

Objective

To analyze the search and identification data of HIV/AIDS related topics of public concern on TikTok platform to provide evidence for optimizing public health education strategies.

Methods

Based on search data for "HIV" and "AIDS" from TikTok platform from January 2023 to December 2024, the latent dirichlet allocation (LDA) model was employed for topic extraction and evolution analysis.

Results

Four search peaks were observed in March and June 2023, and February and December 2024. The LDA model analysis identified eight core themes: expert-led science communication, visualized explanation of viral mechanisms, treatment and patient support, sexual and reproductive health, antiviral treatment and HIV/AIDS prevention campaigns, prenatal HIV screening, public awareness activities, and HIV/AIDS related film and television narratives. Thematic evolution followed two primary pathways: HIV prevention and prenatal cultural representation.

Conclusions

Public concern to AIDS on TikTok platform is primarily driven by three factors: public health campaigns, societal activity cycles and social media-amplified events. The users exhibit dual demands for scientifically rigorous yet visually accessible content and thematic shifts are largely shaped by policy-driven agenda setting. It is recommended that future HIV/AIDS health communication strategies be optimized across multiple dimensions, including message source credibility, content creation, communication formats, and strategic timing, to enhance public engagement and health literacy.

Для цитирования: Zhang Y, Luo D H, Dong W, Xu J, Xu J F, Zhou C. [Analysis on HIV/AIDS related topics of public concern on TikTok platform: based on latent dirichlet allocation model].. Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi. 2026. DOI: 10.3760/cma.j.cn112338-20250626-00438.

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