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.
BibTeX
@inproceedings{valizadeh-etal-2021-identifying,
title = "Identifying Medical Self-Disclosure in Online Communities",
author = "Valizadeh, Mina and
Ranjbar-Noiey, Pardis and
Caragea, Cornelia and
Parde, Natalie",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.347/",
doi = "10.18653/v1/2021.naacl-main.347",
pages = "4398--4408"
}