Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model

• 成果类型: Article
• 署名作者: Xie, Jiaheng; Chai, Yidong; Liang, Ruicheng; Liu, Yang; Zeng, Daniel Dajun
• 署名单位: University of Delaware; Hefei University of Technology; City University of Hong Kong; Anhui University of Finance & Economics; Chinese Academy of Sciences; Institute of Automation, CAS; Nanjing Institute of Geology & Paleontology, CAS
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047; 1526-5536
• DOI: 10.1287/isre.2024.1071
• 发表日期: 2026-03
• 关键词: short-form video; suicidal thought impact; neural topic model; design science; prediction; Social media; DEPRESSION

摘要

With the rise of short-form videos, the mental impact on viewers has led to widespread consequences, prompting platforms to predict videos' impact on viewers' mental health. Subsequently, platforms can take intervention measures according to their community guidelines. Nevertheless, applicable predictive methods lack relevance to wellestablished medical knowledge, which outlines clinically proven external and environmental factors of mental disorders. To account for such medical knowledge, we resort to an emergent methodological discipline: seeded neural topic models (NTMs). However, existing seeded NTMs suffer from the limitations of single-origin topics, unknown topic sources, unclear seed supervision, and suboptimal convergence. To address those challenges, we develop a novel knowledge-guided NTM to predict a short-form video's suicidal thought impact on viewers. Extensive empirical analyses using two short-form video platforms prove that our method outperforms state-of-the-art benchmarks. Our method also discovers medically relevant topics from videos that are linked to suicidal thought impact. We contribute to information systems with a novel video analytics method that is generalizable to other video classification problems. Practically, our method can help platforms understand videos' suicidal thought impacts, thus moderating videos that violate their community guidelines.