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

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 well-established 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.

History: Gautam Pant, Senior Editor; Gene Moo Lee, Associate Editor.

Funding: Y. Chai is supported by the National Natural Science Foundation of China [Grants 72342011, 72110107003, 72101079, and 72188101]. D. D. Zeng is supported by the National Natural Science Foundation of China [Grant 72293575]. R. Liang is supported by the National Natural Science Foundation of China [Grant 72402001] and the Natural Science Research Project of Anhui Educational Committee [Grant 2024AH050010]. J. Xie is not supported by any funds and is not associated with any of the above mentioned funds.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2024.1071.