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.- Anthology ID:
- 2021.ranlp-1.24
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
- Month:
- September
- Year:
- 2021
- Address:
- Held Online
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 199–207
- Language:
- URL:
- https://aclanthology.org/2021.ranlp-1.24
- DOI:
- Cite (ACL):
- Ana-Maria Bucur, Ioana R. Podina, and Liviu P. Dinu. 2021. A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 199–207, Held Online. INCOMA Ltd..
- Cite (Informal):
- A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media (Bucur et al., RANLP 2021)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2021.ranlp-1.24.pdf