A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media

• URL: http://arxiv.org/abs/2108.00279v1
• Date: Sat, 31 Jul 2021 16:23:22 GMT
• Title: A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media
• Authors: Ana-Maria Bucur, Ioana R. Podin\u{a} and Liviu P. Dinu
• Abstract summary: We use the depression dataset from the Early Risk Prediction on the Internet Workshop (eRisk) 2018. Our results reveal statistically significant differences between the depressed and non-depressed individuals. Our work provides insights regarding the way in which depressed individuals are expressing themselves on social media platforms.
• Score: 1.7188280334580193
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we provide an extensive part-of-speech analysis of the discourse of social media users with depression. Research in psychology revealed that depressed users tend to be self-focused, more preoccupied with themselves and ruminate more about their lives and emotions. Our work aims to make use of large-scale datasets and computational methods for a quantitative exploration of discourse. We use the publicly available depression dataset from the Early Risk Prediction on the Internet Workshop (eRisk) 2018 and extract part-of-speech features and several indices based on them. Our results reveal statistically significant differences between the depressed and non-depressed individuals confirming findings from the existing psychology literature. Our work provides insights regarding the way in which depressed individuals are expressing themselves on social media platforms, allowing for better-informed computational models to help monitor and prevent mental illnesses.

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