Cordyceps@LT-EDI: Depression Detection with Reddit and Self-training

• URL: http://arxiv.org/abs/2310.01418v1
• Date: Sun, 24 Sep 2023 01:14:49 GMT
• Title: Cordyceps@LT-EDI: Depression Detection with Reddit and Self-training
• Authors: Dean Ninalga
• Abstract summary: Depression is debilitating, and not uncommon. Indeed, studies of excessive social media users show correlations with depression, ADHD, and other mental health concerns. We propose a severity depression detection system using a semi-supervised learning technique to predict if a post is from a user who is experiencing severe, moderate, or low levels of depression.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Depression is debilitating, and not uncommon. Indeed, studies of excessive social media users show correlations with depression, ADHD, and other mental health concerns. Given that there is a large number of people with excessive social media usage, then there is a significant population of potentially undiagnosed users and posts that they create. In this paper, we propose a depression severity detection system using a semi-supervised learning technique to predict if a post is from a user who is experiencing severe, moderate, or low (non-diagnostic) levels of depression. Namely, we use a trained model to classify a large number of unlabelled social media posts from Reddit, then use these generated labels to train a more powerful classifier. We demonstrate our framework on Detecting Signs of Depression from Social Media Text - LT-EDI@RANLP 2023 shared task, where our framework ranks 3rd overall.

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