Same Vaccine, Different Voices: A Cross-Modality Analysis of HPV Vaccine Discourse on Social Media
Despite the proven efficacy of HPV vaccines, uptake remains limited in many regions, including China. This study investigates how health beliefs and emotional responses evolve across text-, audio-, and video-based platforms by analyzing data from three representative platforms in China, including 273,357 posts from Weibo (text-based), 1,228 podcasts from Ximalaya (audio-based), and 1,225 videos from Douyin (video-based) from July 2018 to March 2023. The comparisons are conducted under four dimensions as suggested by the Health Belief Model (HBM), including susceptibility, severity, benefits, and barriers. Our findings reveal distinct modality-specific patterns. For instance, a text-based platform tends to amplify barriers and negativity, an audio-based platform enables balanced and sustained discussions, and a video-based platform highlights personal anecdotes and drives rapid sentiment shifts. By highlighting these modality-specific differences and addressing potential cross-modal incongruities at the content level, we provide actionable insights for public health communicators, policymakers, and platform designers to tailor strategies, foster informed decision-making, and ultimately enhance HPV vaccine uptake in complex social media ecosystems.
Authors
- Rui Su (ORCID: https://orcid.org/0000-0001-8386-7408)
- Mengxiao Zhu (ORCID: https://orcid.org/0000-0003-3596-5585)
- He Liu (ORCID: https://orcid.org/0000-0001-9671-6457)
- Licheng Zhang (ORCID: https://orcid.org/0000-0003-3629-4605)
- Han Zhao (ORCID: https://orcid.org/0000-0001-6770-5396)
- Bo Hu (ORCID: https://orcid.org/0000-0003-1462-8854)
Institutions
- University of Science and Technology of China (CN)
- The University of Texas at Austin (US)
- Anhui University of Technology (CN)
Publication Details
- Journal
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2025-06-07
- DOI
- https://doi.org/10.1609/icwsm.v19i1.35936
- Primary Topic
- Hate Speech and Cyberbullying Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- University of Science and Technology of China