论文标题
通过在YouTube视频中检测抑郁来评估观众的心理健康
Assessing Viewer's Mental Health by Detecting Depression in YouTube Videos
论文作者
论文摘要
抑郁症是世界上最普遍的心理健康问题之一，被证明是自杀的主要原因之一，并给家庭和社会带来了巨大的经济负担。在本文中，我们开发和测试了应用于通过成绩单捕获的YouTube视频内容的机器学习技术的功效，并确定视频是抑郁症还是具有令人沮丧的触发因素。我们的模型可以以83％的精度检测抑郁视频。我们还介绍了一种现实生活中的评估技术，以根据视频中发布的评论来验证我们的分类，通过计算评论的CES-D分数。这项工作极大地符合联合国可持续目标，即确保健康状况良好，并与UN SDG 3.4节的重要性保持健康。
Depression is one of the most prevalent mental health issues around the world, proving to be one of the leading causes of suicide and placing large economic burdens on families and society. In this paper, we develop and test the efficacy of machine learning techniques applied to the content of YouTube videos captured through their transcripts and determine if the videos are depressive or have a depressing trigger. Our model can detect depressive videos with an accuracy of 83%. We also introduce a real-life evaluation technique to validate our classification based on the comments posted on a video by calculating the CES-D scores of the comments. This work conforms greatly with the UN Sustainable Goal of ensuring Good Health and Well Being with major conformity with section UN SDG 3.4.
