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

时间：2024年6月17日 16:00-17:30

地点：中关村校区主楼317

报告人：柴一栋教授

报告人简介：

柴一栋，合肥工业大学教授，博士生导师。本科毕业于12BET信息管理与信息系统专业，博士毕业于清华大学经管学院管理科学与工程系，主要研究信息系统安全与网络空间管理、智慧医疗管理、商务智能管理等。以第一作者或通讯作者发表研究成果于MISQ、ISR、JMIS、IEEE TDSC、IEEE TPAMI、IEEE TKDE等国际顶级期刊。发表学术专著一部，授权专利多项。主持国家优秀青年基金等项目。获全国首届数据空间大会优秀科技成果奖、国际信息系统权威会议WITS 2021 best paper award等荣誉。

报告内容简介：

While short-form videos head to reshape the entire social media landscape, experts are exceedingly worried about their depressive impacts on viewers. To prevent widespread consequences, platforms are eager to predict these videos’ impact on viewers’ mental health. Subsequently, they can take intervention measures, such as revising recommendation algorithms and displaying viewer discretion. Nevertheless, applicable predictive methods lack relevance to well-established medical knowledge, which outlines clinically proven external and environmental factors of depression. 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 depressive impact on viewers. Extensive empirical analyses using TikTok and Douyin datasets prove that our method outperforms state-of-the-art benchmarks. Our method also discovers medically relevant topics from videos that are linked to depressive 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’ mental impacts, thus adjusting recommendations and video topic disclosure.

（承办：管理工程系、科研与学术交流中心）