Cited by 22Mental Health via WritingSentiment Analysis and Opinion MiningEmotion and Mood Recognition

Explainable depression detection using large language models on social media data

Open access
Wang, Yuxi, Inkpen, Diana, Kirinde Gamaarachchige, Prasadith·2024·Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)

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 provide 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.Question Rephrased symptom Q1 how sad the user feels Q2 how discouraged the user is about future Q3 how much the user feels like a failure Q4 how much the user loses pleasure from things Q5 how often the user feels guilty Q6 how much the user feels punished Q7 how much the user feels disappointed about him/herself Q8 how often the user criticizes or blames him/herself Q9 how much the user thinks about killing him/herself Q10 how often the user cries Q11 how much the user feels restless or agitated Q12 how much the user loses interest in things Q13 how difficult the user to make decisions Q14 how much the user feels worthless Q15 how much the user loses energy Q16 how much the user experienced changes in sleeping Q17 how much the user feels irritable Q18 how much the user experienced changes in appetite Q19 how difficult the user to concentrate Q20 how much the user feels tired or fatigued Q21 how much the user loses interest in sexTable A5: Rephrased symptoms on the BDI questionnaire

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Crossref · 10.18653/v1/2024.clpsych-1.8

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  • A first group anchors generated explanations directly to questionnaire items or diagnostic criteria, so that each rationale element traces back to a recognized clinical entry.

    “A first group anchors generated explanations directly to questionnaire items or diagnostic criteria, so that each rationale element traces back to a recognized clinical entry such as the Beck Depression Inventory and the DSM-5 [68,71], the PHQ-9 [70], and standardized psychological scales as a class [115].”

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Authors
Wang, Y., Inkpen, D., & Kirinde Gamaarachchige, P.
Affiliations
University of Ottawa (CA)
Venue
Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
Published
2024
Pages
108-126
OpenAlex
W7126391127
Cited by
22
References
0
Citations by year
2026: 20 · 2025: 1 · 2024: 1
FWCI
14.75
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
Workshop on Computational Linguistics and Clinical Psychology (CLPsych) (publishedVersion, cc-by)
Funders
Natural Sciences and Engineering Research Council of Canada
Topics
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
Keywords
depression detection, large language models, explainable AI, Beck Depression Inventory, social media text analysis, Reddit, mental disorder screening

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