They Look Like Each Other: Case-based Reasoning for Explainable Depression Detection on Twitter using Large Language Models
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917737985474560 |
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| author | Mahdavinejad, Mohammad Saeid Adibi, Peyman Monadjemi, Amirhassan Hitzler, Pascal |
| author_facet | Mahdavinejad, Mohammad Saeid Adibi, Peyman Monadjemi, Amirhassan Hitzler, Pascal |
| contents | Depression is a common mental health issue that requires prompt diagnosis and treatment. Despite the promise of social media data for depression detection, the opacity of employed deep learning models hinders interpretability and raises bias concerns. We address this challenge by introducing ProtoDep, a novel, explainable framework for Twitter-based depression detection. ProtoDep leverages prototype learning and the generative power of Large Language Models to provide transparent explanations at three levels: (i) symptom-level explanations for each tweet and user, (ii) case-based explanations comparing the user to similar individuals, and (iii) transparent decision-making through classification weights. Evaluated on five benchmark datasets, ProtoDep achieves near state-of-the-art performance while learning meaningful prototypes. This multi-faceted approach offers significant potential to enhance the reliability and transparency of depression detection on social media, ultimately aiding mental health professionals in delivering more informed care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_21041 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | They Look Like Each Other: Case-based Reasoning for Explainable Depression Detection on Twitter using Large Language Models Mahdavinejad, Mohammad Saeid Adibi, Peyman Monadjemi, Amirhassan Hitzler, Pascal Computation and Language Artificial Intelligence Social and Information Networks Depression is a common mental health issue that requires prompt diagnosis and treatment. Despite the promise of social media data for depression detection, the opacity of employed deep learning models hinders interpretability and raises bias concerns. We address this challenge by introducing ProtoDep, a novel, explainable framework for Twitter-based depression detection. ProtoDep leverages prototype learning and the generative power of Large Language Models to provide transparent explanations at three levels: (i) symptom-level explanations for each tweet and user, (ii) case-based explanations comparing the user to similar individuals, and (iii) transparent decision-making through classification weights. Evaluated on five benchmark datasets, ProtoDep achieves near state-of-the-art performance while learning meaningful prototypes. This multi-faceted approach offers significant potential to enhance the reliability and transparency of depression detection on social media, ultimately aiding mental health professionals in delivering more informed care. |
| title | They Look Like Each Other: Case-based Reasoning for Explainable Depression Detection on Twitter using Large Language Models |
| topic | Computation and Language Artificial Intelligence Social and Information Networks |
| url | https://arxiv.org/abs/2407.21041 |