Assessing Viewer's Mental Health by Detecting Depression in YouTube Videos
This work addresses mental health monitoring for online viewers by providing a tool to identify depressive content, though it is incremental as it applies existing methods to a new domain.
The paper tackles detecting depression in YouTube videos using machine learning on transcripts, achieving 83% accuracy in classifying depressive content, and validates this with a real-life evaluation technique based on comment analysis using CES-D scores.
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.