報告題目：Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model
時間：2024年6月17日 16:00-17:30
地點：中關村校區(qū)主樓317
報告人：柴一棟教授
報告人簡介：
柴一棟，合肥工業(yè)大學教授，博士生導師。本科畢業(yè)于偉德國際1946bv官網信息管理與信息系統(tǒng)專業(yè)，博士畢業(yè)于清華大學經管學院管理科學與工程系，主要研究信息系統(tǒng)安全與網絡空間管理、智慧醫(yī)療管理、商務智能管理等。以第一作者或通訊作者發(fā)表研究成果于MISQ、ISR、JMIS、IEEE TDSC、IEEE TPAMI、IEEE TKDE等國際頂級期刊。發(fā)表學術專著一部，授權專利多項。主持國家優(yōu)秀青年基金等項目。獲全國首屆數據空間大會優(yōu)秀科技成果獎、國際信息系統(tǒng)權威會議WITS 2021 best paper award等榮譽。
報告內容簡介：
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.
（承辦：管理工程系、科研與學術交流中心）
