@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",
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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="valizadeh-etal-2021-identifying">
<titleInfo>
<title>Identifying Medical Self-Disclosure in Online Communities</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mina</namePart>
<namePart type="family">Valizadeh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pardis</namePart>
<namePart type="family">Ranjbar-Noiey</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Cornelia</namePart>
<namePart type="family">Caragea</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Natalie</namePart>
<namePart type="family">Parde</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2021-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kristina</namePart>
<namePart type="family">Toutanova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anna</namePart>
<namePart type="family">Rumshisky</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Luke</namePart>
<namePart type="family">Zettlemoyer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dilek</namePart>
<namePart type="family">Hakkani-Tur</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Iz</namePart>
<namePart type="family">Beltagy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Steven</namePart>
<namePart type="family">Bethard</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ryan</namePart>
<namePart type="family">Cotterell</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tanmoy</namePart>
<namePart type="family">Chakraborty</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yichao</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">valizadeh-etal-2021-identifying</identifier>
<identifier type="doi">10.18653/v1/2021.naacl-main.347</identifier>
<location>
<url>https://aclanthology.org/2021.naacl-main.347</url>
</location>
<part>
<date>2021-06</date>
<extent unit="page">
<start>4398</start>
<end>4408</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Identifying Medical Self-Disclosure in Online Communities
%A Valizadeh, Mina
%A Ranjbar-Noiey, Pardis
%A Caragea, Cornelia
%A Parde, Natalie
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F valizadeh-etal-2021-identifying
%X 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.
%R 10.18653/v1/2021.naacl-main.347
%U https://aclanthology.org/2021.naacl-main.347
%U https://doi.org/10.18653/v1/2021.naacl-main.347
%P 4398-4408
Markdown (Informal)
[Identifying Medical Self-Disclosure in Online Communities](https://aclanthology.org/2021.naacl-main.347) (Valizadeh et al., NAACL 2021)
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.