Identifying Medical Self-Disclosure in Online Communities
Mina Valizadeh, Pardis Ranjbar-Noiey, Cornelia Caragea, Natalie Parde
Abstract
Self-disclosure in online health conversations may offer a host of benefits, including earlier detection and treatment of medical issues that may have otherwise gone unaddressed. However, research analyzing medical self-disclosure in online communities is limited. We address this shortcoming by introducing a new dataset of health-related posts collected from online social platforms, categorized into three groups (No Self-Disclosure, Possible Self-Disclosure, and Clear Self-Disclosure) with high inter-annotator agreement (_k_=0.88). We make this data available to the research community. We also release a predictive model trained on this dataset that achieves an accuracy of 81.02%, establishing a strong performance benchmark for this task.- Anthology ID:
- 2021.naacl-main.347
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4398–4408
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.347
- DOI:
- 10.18653/v1/2021.naacl-main.347
- Cite (ACL):
- Mina Valizadeh, Pardis Ranjbar-Noiey, Cornelia Caragea, and Natalie Parde. 2021. Identifying Medical Self-Disclosure in Online Communities. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4398–4408, Online. Association for Computational Linguistics.
- Cite (Informal):
- Identifying Medical Self-Disclosure in Online Communities (Valizadeh et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2021.naacl-main.347.pdf