@inproceedings{zanwar-etal-2022-best,
title = "The Best of Both Worlds: Combining Engineered Features with Transformers for Improved Mental Health Prediction from {R}eddit Posts",
author = "Zanwar, Sourabh and
Wiechmann, Daniel and
Qiao, Yu and
Kerz, Elma",
editor = "Gonzalez-Hernandez, Graciela and
Weissenbacher, Davy",
booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.smm4h-1.50",
pages = "197--202",
abstract = "In recent years, there has been increasing interest in the application of natural language processing and machine learning techniques to the detection of mental health conditions (MHC) based on social media data. In this paper, we aim to improve the state-of-the-art (SoTA) detection of six MHC in Reddit posts in two ways: First, we built models leveraging Bidirectional Long Short-Term Memory (BLSTM) networks trained on in-text distributions of a comprehensive set of psycholinguistic features for more explainable MHC detection as compared to black-box solutions. Second, we combine these BLSTM models with Transformers to improve the prediction accuracy over SoTA models. In addition, we uncover nuanced patterns of linguistic markers characteristic of specific MHC.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zanwar-etal-2022-best">
<titleInfo>
<title>The Best of Both Worlds: Combining Engineered Features with Transformers for Improved Mental Health Prediction from Reddit Posts</title>
</titleInfo>
<name type="personal">
<namePart type="given">Sourabh</namePart>
<namePart type="family">Zanwar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="family">Wiechmann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yu</namePart>
<namePart type="family">Qiao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elma</namePart>
<namePart type="family">Kerz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2022-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task</title>
</titleInfo>
<name type="personal">
<namePart type="given">Graciela</namePart>
<namePart type="family">Gonzalez-Hernandez</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Davy</namePart>
<namePart type="family">Weissenbacher</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Gyeongju, Republic of Korea</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In recent years, there has been increasing interest in the application of natural language processing and machine learning techniques to the detection of mental health conditions (MHC) based on social media data. In this paper, we aim to improve the state-of-the-art (SoTA) detection of six MHC in Reddit posts in two ways: First, we built models leveraging Bidirectional Long Short-Term Memory (BLSTM) networks trained on in-text distributions of a comprehensive set of psycholinguistic features for more explainable MHC detection as compared to black-box solutions. Second, we combine these BLSTM models with Transformers to improve the prediction accuracy over SoTA models. In addition, we uncover nuanced patterns of linguistic markers characteristic of specific MHC.</abstract>
<identifier type="citekey">zanwar-etal-2022-best</identifier>
<location>
<url>https://aclanthology.org/2022.smm4h-1.50</url>
</location>
<part>
<date>2022-10</date>
<extent unit="page">
<start>197</start>
<end>202</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T The Best of Both Worlds: Combining Engineered Features with Transformers for Improved Mental Health Prediction from Reddit Posts
%A Zanwar, Sourabh
%A Wiechmann, Daniel
%A Qiao, Yu
%A Kerz, Elma
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%S Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F zanwar-etal-2022-best
%X In recent years, there has been increasing interest in the application of natural language processing and machine learning techniques to the detection of mental health conditions (MHC) based on social media data. In this paper, we aim to improve the state-of-the-art (SoTA) detection of six MHC in Reddit posts in two ways: First, we built models leveraging Bidirectional Long Short-Term Memory (BLSTM) networks trained on in-text distributions of a comprehensive set of psycholinguistic features for more explainable MHC detection as compared to black-box solutions. Second, we combine these BLSTM models with Transformers to improve the prediction accuracy over SoTA models. In addition, we uncover nuanced patterns of linguistic markers characteristic of specific MHC.
%U https://aclanthology.org/2022.smm4h-1.50
%P 197-202
Markdown (Informal)
[The Best of Both Worlds: Combining Engineered Features with Transformers for Improved Mental Health Prediction from Reddit Posts](https://aclanthology.org/2022.smm4h-1.50) (Zanwar et al., SMM4H 2022)
ACL