Detecting mental disorder on social media: a ChatGPT-augmented explainable approach
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866911046032162816 |
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| author | Belcastro, Loris Cantini, Riccardo Marozzo, Fabrizio Talia, Domenico Trunfio, Paolo |
| author_facet | Belcastro, Loris Cantini, Riccardo Marozzo, Fabrizio Talia, Domenico Trunfio, Paolo |
| contents | In the digital era, the prevalence of depressive symptoms expressed on social media has raised serious concerns, necessitating advanced methodologies for timely detection. This paper addresses the challenge of interpretable depression detection by proposing a novel methodology that effectively combines Large Language Models (LLMs) with eXplainable Artificial Intelligence (XAI) and conversational agents like ChatGPT. In our methodology, explanations are achieved by integrating BERTweet, a Twitter-specific variant of BERT, into a novel self-explanatory model, namely BERT-XDD, capable of providing both classification and explanations via masked attention. The interpretability is further enhanced using ChatGPT to transform technical explanations into human-readable commentaries. By introducing an effective and modular approach for interpretable depression detection, our methodology can contribute to the development of socially responsible digital platforms, fostering early intervention and support for mental health challenges under the guidance of qualified healthcare professionals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17477 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detecting mental disorder on social media: a ChatGPT-augmented explainable approach Belcastro, Loris Cantini, Riccardo Marozzo, Fabrizio Talia, Domenico Trunfio, Paolo Computation and Language Artificial Intelligence Machine Learning Social and Information Networks In the digital era, the prevalence of depressive symptoms expressed on social media has raised serious concerns, necessitating advanced methodologies for timely detection. This paper addresses the challenge of interpretable depression detection by proposing a novel methodology that effectively combines Large Language Models (LLMs) with eXplainable Artificial Intelligence (XAI) and conversational agents like ChatGPT. In our methodology, explanations are achieved by integrating BERTweet, a Twitter-specific variant of BERT, into a novel self-explanatory model, namely BERT-XDD, capable of providing both classification and explanations via masked attention. The interpretability is further enhanced using ChatGPT to transform technical explanations into human-readable commentaries. By introducing an effective and modular approach for interpretable depression detection, our methodology can contribute to the development of socially responsible digital platforms, fostering early intervention and support for mental health challenges under the guidance of qualified healthcare professionals. |
| title | Detecting mental disorder on social media: a ChatGPT-augmented explainable approach |
| topic | Computation and Language Artificial Intelligence Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2401.17477 |