讲座题目：Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model（短视频与心理健康：一种知识引导的神经主题模型）

主讲人：柴一栋 香港城市大学

讲座时间：2025年12月19日10:30

讲座地点：学院216

内容摘要：

Along with the rise of short-form videos, their mental impacts on viewers have led to widespread consequences, prompting platforms to predict videos' impact on viewers' mental health. Subsequently, they 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 short-form videos such as Douyin 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 IS 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.

随着短视频的兴起，其对观众心理健康的影响引发广泛社会关注，促使各大平台开始预测视频内容对观众心理健康的潜在影响，并根据社区准则采取干预措施。然而，现有的预测方法往往缺乏与临床医学知识的关联，而这些知识系统阐述经临床验证的精神障碍外在与环境诱因。为融入医学专业知识，团队引入一种新兴方法学分支——种子神经主题模型。但现有种子神经主题模型存在主题来源单一、主题溯源困难、种子监督机制不明确及收敛效果欠佳等局限性。为克服这些挑战，团队开发了一种新型知识引导神经主题模型，专门用于预测短视频内容对观众意念的影响。通过对抖音等平台短视频的大规模实证分析表明，该方法在预测性能上超越当前最先进的基准模型。同时，该方法能从视频内容中识别出某些医学主题，为信息系统领域贡献了可推广至其他视频分类问题的创新分析方法。在实践中，该方法能帮助平台有效识别具有心理诱导风险的视频内容，从而对违反社区准则的视频进行及时管控。

主讲人简介：

柴一栋，香港城市大学长聘副教授，合肥工业大学博士生导师，国家高层次青年人才。博士毕业于清华大学经管学院管理科学与工程系（信息系统方向），主要关注如何设计创新性的人工智能方法，更好地服务于个人、组织和社会的现代科学化管理。以第一作者或通讯作者发表研究成果于MISQ、ISR、JMIS等管理信息系统顶刊（UTD/FT），IEEE TDSC、IEEE TPAMI、IEEE TKDE、ACM TOIS等信息安全/人工智能/数据挖掘等领域的期刊（CCF A），以及《管理科学学报》等中文期刊。担任ACM Transactions on AI Security and Privacy、《系统科学与系统工程学报》（英文版）、Industrial Management & Data Systems等期刊副主编。