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
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
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/author-url/2021.naacl-main.347.pdf
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