A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media
This work addresses the need for better computational models to monitor and prevent mental illnesses by providing quantitative insights into depression expression on social media, though it is incremental as it confirms existing psychology literature.
The paper tackled the problem of analyzing discourse patterns of depressed individuals on social media by conducting a part-of-speech analysis, confirming statistically significant differences between depressed and non-depressed users based on psychological insights.
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.