Leveraging Mental Health Forums for User-level Depression Detection on Social Media
Sravani Boinepelli, Tathagata Raha, Harika Abburi, Pulkit Parikh, Niyati Chhaya, Vasudeva Varma
Abstract
The number of depression and suicide risk cases on social media platforms is ever-increasing, and the lack of depression detection mechanisms on these platforms is becoming increasingly apparent. A majority of work in this area has focused on leveraging linguistic features while dealing with small-scale datasets. However, one faces many obstacles when factoring into account the vastness and inherent imbalance of social media content. In this paper, we aim to optimize the performance of user-level depression classification to lessen the burden on computational resources. The resulting system executes in a quicker, more efficient manner, in turn making it suitable for deployment. To simulate a platform agnostic framework, we simultaneously replicate the size and composition of social media to identify victims of depression. We systematically design a solution that categorizes post embeddings, obtained by fine-tuning transformer models such as RoBERTa, and derives user-level representations using hierarchical attention networks. We also introduce a novel mental health dataset to enhance the performance of depression categorization. We leverage accounts of depression taken from this dataset to infuse domain-specific elements into our framework. Our proposed methods outperform numerous baselines across standard metrics for the task of depression detection in text.- Anthology ID:
- 2022.lrec-1.580
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 5418–5427
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.580
- DOI:
- Cite (ACL):
- Sravani Boinepelli, Tathagata Raha, Harika Abburi, Pulkit Parikh, Niyati Chhaya, and Vasudeva Varma. 2022. Leveraging Mental Health Forums for User-level Depression Detection on Social Media. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 5418–5427, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Leveraging Mental Health Forums for User-level Depression Detection on Social Media (Boinepelli et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.580.pdf