@inproceedings{wang-etal-2024-explainable,
title = "Explainable Depression Detection Using Large Language Models on Social Media Data",
author = "Wang, Yuxi and
Inkpen, Diana and
Kirinde Gamaarachchige, Prasadith",
editor = "Yates, Andrew and
Desmet, Bart and
Prud{'}hommeaux, Emily and
Zirikly, Ayah and
Bedrick, Steven and
MacAvaney, Sean and
Bar, Kfir and
Ireland, Molly and
Ophir, Yaakov",
booktitle = "Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clpsych-1.8",
pages = "108--126",
abstract = "Due to the rapid growth of user interaction on different social media platforms, publicly available social media data has increased substantially. The sheer amount of data and level of personal information being shared on such platforms has made analyzing textual information to predict mental disorders such as depression a reliable preliminary step when it comes to psychometrics. In this study, we first proposed a system to search for texts that are related to depression symptoms from the Beck{'}s Depression Inventory (BDI) questionnaire, and providing a ranking for further investigation in a second step. Then, in this second step, we address the even more challenging task of automatic depression level detection, using writings and voluntary answers provided by users on Reddit. Several Large Language Models (LLMs) were applied in experiments. Our proposed system based on LLMs can generate both predictions and explanations for each question. By combining two LLMs for different questions, we achieved better performance on three of four metrics compared to the state-of-the-art and remained competitive on the one remaining metric. In addition, our system is explainable on two levels: first, knowing the answers to the BDI questions provides clues about the possible symptoms that could lead to a clinical diagnosis of depression; second, our system can explain the predicted answer for each question.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wang-etal-2024-explainable">
<titleInfo>
<title>Explainable Depression Detection Using Large Language Models on Social Media Data</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yuxi</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Diana</namePart>
<namePart type="family">Inkpen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Prasadith</namePart>
<namePart type="family">Kirinde Gamaarachchige</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Andrew</namePart>
<namePart type="family">Yates</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Bart</namePart>
<namePart type="family">Desmet</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Emily</namePart>
<namePart type="family">Prud’hommeaux</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ayah</namePart>
<namePart type="family">Zirikly</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Steven</namePart>
<namePart type="family">Bedrick</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sean</namePart>
<namePart type="family">MacAvaney</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kfir</namePart>
<namePart type="family">Bar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Molly</namePart>
<namePart type="family">Ireland</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yaakov</namePart>
<namePart type="family">Ophir</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">St. Julians, Malta</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Due to the rapid growth of user interaction on different social media platforms, publicly available social media data has increased substantially. The sheer amount of data and level of personal information being shared on such platforms has made analyzing textual information to predict mental disorders such as depression a reliable preliminary step when it comes to psychometrics. In this study, we first proposed a system to search for texts that are related to depression symptoms from the Beck’s Depression Inventory (BDI) questionnaire, and providing a ranking for further investigation in a second step. Then, in this second step, we address the even more challenging task of automatic depression level detection, using writings and voluntary answers provided by users on Reddit. Several Large Language Models (LLMs) were applied in experiments. Our proposed system based on LLMs can generate both predictions and explanations for each question. By combining two LLMs for different questions, we achieved better performance on three of four metrics compared to the state-of-the-art and remained competitive on the one remaining metric. In addition, our system is explainable on two levels: first, knowing the answers to the BDI questions provides clues about the possible symptoms that could lead to a clinical diagnosis of depression; second, our system can explain the predicted answer for each question.</abstract>
<identifier type="citekey">wang-etal-2024-explainable</identifier>
<location>
<url>https://aclanthology.org/2024.clpsych-1.8</url>
</location>
<part>
<date>2024-03</date>
<extent unit="page">
<start>108</start>
<end>126</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Explainable Depression Detection Using Large Language Models on Social Media Data
%A Wang, Yuxi
%A Inkpen, Diana
%A Kirinde Gamaarachchige, Prasadith
%Y Yates, Andrew
%Y Desmet, Bart
%Y Prud’hommeaux, Emily
%Y Zirikly, Ayah
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ireland, Molly
%Y Ophir, Yaakov
%S Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F wang-etal-2024-explainable
%X Due to the rapid growth of user interaction on different social media platforms, publicly available social media data has increased substantially. The sheer amount of data and level of personal information being shared on such platforms has made analyzing textual information to predict mental disorders such as depression a reliable preliminary step when it comes to psychometrics. In this study, we first proposed a system to search for texts that are related to depression symptoms from the Beck’s Depression Inventory (BDI) questionnaire, and providing a ranking for further investigation in a second step. Then, in this second step, we address the even more challenging task of automatic depression level detection, using writings and voluntary answers provided by users on Reddit. Several Large Language Models (LLMs) were applied in experiments. Our proposed system based on LLMs can generate both predictions and explanations for each question. By combining two LLMs for different questions, we achieved better performance on three of four metrics compared to the state-of-the-art and remained competitive on the one remaining metric. In addition, our system is explainable on two levels: first, knowing the answers to the BDI questions provides clues about the possible symptoms that could lead to a clinical diagnosis of depression; second, our system can explain the predicted answer for each question.
%U https://aclanthology.org/2024.clpsych-1.8
%P 108-126
Markdown (Informal)
[Explainable Depression Detection Using Large Language Models on Social Media Data](https://aclanthology.org/2024.clpsych-1.8) (Wang et al., CLPsych-WS 2024)
ACL