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    <titleInfo>
        <title>Mental Disorder Classification via Temporal Representation of Text</title>
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        <namePart type="given">Kishan</namePart>
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    <name type="personal">
        <namePart type="given">Ashita</namePart>
        <namePart type="family">Saxena</namePart>
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        <namePart type="given">Pushpak</namePart>
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            <namePart type="given">Mohit</namePart>
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    <abstract>Mental disorders pose a global challenge, aggravated by the shortage of qualified mental health professionals. Mental disorder prediction from social media posts by current LLMs is challenging due to the complexities of sequential text data and the limited context length of language models. Current language model-based approaches split a single data instance into multiple chunks to compensate for limited context size. The predictive model is then applied to each chunk individually, and the most voted output is selected as the final prediction. This results in the loss of inter-post dependencies and important time variant information, leading to poor performance. We propose a novel framework which first compresses the large sequence of chronologically ordered social media posts into a series of numbers. We then use this time variant representation for mental disorder classification. We demonstrate the generalization capabilities of our framework by outperforming the current SOTA in three different mental conditions: depression, self-harm, and anorexia, by an absolute improvement of 5% in the F1 score. We also investigate the situation when current data instances fall within the context length of language models and present empirical results highlighting the importance of temporal properties of textual data. Furthermore, we utilize the proposed framework for a cross-domain study, exploring commonalities across disorders and the possibility of inter-domain data usage.</abstract>
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%0 Conference Proceedings
%T Mental Disorder Classification via Temporal Representation of Text
%A Kumar, Raja
%A Maharaj, Kishan
%A Saxena, Ashita
%A Bhattacharyya, Pushpak
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kumar-etal-2024-mental
%X Mental disorders pose a global challenge, aggravated by the shortage of qualified mental health professionals. Mental disorder prediction from social media posts by current LLMs is challenging due to the complexities of sequential text data and the limited context length of language models. Current language model-based approaches split a single data instance into multiple chunks to compensate for limited context size. The predictive model is then applied to each chunk individually, and the most voted output is selected as the final prediction. This results in the loss of inter-post dependencies and important time variant information, leading to poor performance. We propose a novel framework which first compresses the large sequence of chronologically ordered social media posts into a series of numbers. We then use this time variant representation for mental disorder classification. We demonstrate the generalization capabilities of our framework by outperforming the current SOTA in three different mental conditions: depression, self-harm, and anorexia, by an absolute improvement of 5% in the F1 score. We also investigate the situation when current data instances fall within the context length of language models and present empirical results highlighting the importance of temporal properties of textual data. Furthermore, we utilize the proposed framework for a cross-domain study, exploring commonalities across disorders and the possibility of inter-domain data usage.
%U https://aclanthology.org/2024.findings-emnlp.639
%P 10901-10916

Markdown (Informal)

[Mental Disorder Classification via Temporal Representation of Text](https://aclanthology.org/2024.findings-emnlp.639) (Kumar et al., Findings 2024)

• Mental Disorder Classification via Temporal Representation of Text (Kumar et al., Findings 2024)

ACL

• Raja Kumar, Kishan Maharaj, Ashita Saxena, and Pushpak Bhattacharyya. 2024. Mental Disorder Classification via Temporal Representation of Text. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 10901–10916, Miami, Florida, USA. Association for Computational Linguistics.