Summary of Short-form Videos and Mental Health: a Knowledge-guided Neural Topic Model, by Jiaheng Xie et al.
Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model
by Jiaheng Xie, Ruicheng Liang, Yidong Chai, Yang Liu, Daniel Zeng
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Knowledge-Guided Neural Topic Model addresses the limitations of existing seeded models by incorporating clinically proven external and environmental factors of mental disorders. The novel approach is designed to predict a short-form video’s impact on viewers’ mental health, specifically suicidal thoughts. Empirical analyses using TikTok and Douyin datasets demonstrate that the method outperforms state-of-the-art benchmarks, while also discovering medically relevant topics linked to suicidal thought impact. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to analyze videos is being developed! This method helps predict how a short video might affect someone’s mental health. It uses medical knowledge about what can cause mental disorders and combines it with a special type of AI model. Researchers tested this approach using popular social media platforms like TikTok and found that it works better than current methods. The goal is to help video-sharing platforms understand which videos might be harmful, so they can take action to protect their users. |
