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.

翻译：抑郁症是全球最普遍的心理健康问题之一，已被证实是导致自杀的主要原因之一，并对家庭和社会造成巨大的经济负担。本文开发并测试了将机器学习技术应用于YouTube视频内容（通过转录文本获取）的效果，以判断视频是否具有抑郁倾向或包含引发抑郁的触发因素。我们的模型能以83%的准确率检测出抑郁相关视频。此外，我们引入了一种基于视频评论的现实评估方法，通过计算评论的CES-D分数来验证分类结果。本研究高度契合联合国可持续发展目标中关于确保良好健康与福祉的宗旨，尤其符合联合国可持续发展目标3.4的具体要求。