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They Look Like Each Other: Case-based Reasoning for Explainable Depression Detection on Twitter using Large Language Models (2407.21041v1)

Published 21 Jul 2024 in cs.CL, cs.AI, and cs.SI

Abstract: 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 LLMs 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.

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References (53)
  1. Machine learning models to detect anxiety and depression through social media: A scoping review. Computer Methods and Programs in Biomedicine Update, page 100066.
  2. Multi-task learning for mental health using social media text. arXiv preprint arXiv:1712.03538.
  3. Is attention explanation? an introduction to the debate. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3889–3900.
  4. A collaborative approach to identifying social media markers of schizophrenia by employing machine learning and clinical appraisals. Journal of medical Internet research, 19(8):e7956.
  5. Stevie Chancellor and Munmun De Choudhury. 2020. Methods in predictive techniques for mental health status on social media: a critical review. NPJ digital medicine, 3(1):43.
  6. Measuring post-traumatic stress disorder in twitter. In Proceedings of the international AAAI conference on web and social media, volume 8, pages 579–582.
  7. Aron Culotta. 2014. Estimating county health statistics with twitter. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 1335–1344.
  8. Marco Cuturi. 2013. Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems, 26.
  9. ProtoTEx: Explaining model decisions with prototype tensors. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2986–2997, Dublin, Ireland. Association for Computational Linguistics.
  10. Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems. In 2016 IEEE symposium on security and privacy (SP), pages 598–617. IEEE.
  11. Social media as a measurement tool of depression in populations. In Proceedings of the 5th annual ACM web science conference, pages 47–56.
  12. Munmun De Choudhury and Sushovan De. 2014. Mental health discourse on reddit: Self-disclosure, social support, and anonymity. In Proceedings of the international AAAI conference on web and social media, volume 8, pages 71–80.
  13. Predicting depression via social media. In Proceedings of the international AAAI conference on web and social media, volume 7, pages 128–137.
  14. Interpolating between optimal transport and mmd using sinkhorn divergences. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 2681–2690. PMLR.
  15. Trends in u.s. depression prevalence from 2015 to 2020: The widening treatment gap. American Journal of Preventive Medicine, 63(5):726–733.
  16. Cooperative multimodal approach to depression detection in twitter. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 110–117.
  17. Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences, 18:43–49.
  18. Hierarchical attention network for explainable depression detection on twitter aided by metaphor concept mappings. In Proceedings of the 29th International Conference on Computational Linguistics, pages 94–104.
  19. Supervised learning for suicidal ideation detection in online user content. Complexity, 2018.
  20. Mentalbert: Publicly available pretrained language models for mental healthcare. arXiv preprint arXiv:2110.15621.
  21. Detection of mental health from reddit via deep contextualized representations. In Proceedings of the 11th international workshop on health text mining and information analysis, pages 147–156.
  22. The patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic review. General Hospital Psychiatry, 32(4):345–359.
  23. Social media-based interventions for adolescent and young adult mental health: A scoping review. Internet interventions, page 100578.
  24. Accuracy of patient health questionnaire-9 (phq-9) for screening to detect major depression: individual participant data meta-analysis. BMJ, 365.
  25. Sensemood: depression detection on social media. In Proceedings of the 2020 international conference on Multimedia Retrieval, pages 407–411.
  26. Another time point, a different story: one year effects of a social media intervention on the attitudes of young people towards mental health issues. Social psychiatry and psychiatric epidemiology, 49:985–990.
  27. Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.
  28. Feeling bad on facebook: Depression disclosures by college students on a social networking site. Depression and anxiety, 28(6):447–455.
  29. The effectiveness of feature attribution methods and its correlation with automatic evaluation scores. Advances in Neural Information Processing Systems, 34:26422–26436.
  30. Affective and content analysis of online depression communities. IEEE transactions on affective computing, 5(3):217–226.
  31. Improving the generalizability of depression detection by leveraging clinical questionnaires. arXiv preprint arXiv:2204.10432.
  32. Multimodal prototype-enhanced network for few-shot action recognition. arXiv preprint arXiv:2212.04873.
  33. National Institute of Mental Health. 2023. Mental illness.
  34. Diagnosing psychiatric disorders from history of present illness using a large-scale linguistic model. Psychiatry and Clinical Neurosciences.
  35. Perception differences between the depressed and non-depressed users in twitter. In Proceedings of the international AAAI conference on web and social media, volume 7, pages 476–485.
  36. Michael Paul and Mark Dredze. 2011. You are what you tweet: Analyzing twitter for public health. In Proceedings of the international AAAI conference on web and social media, volume 5, pages 265–272.
  37. Learning to deceive with attention-based explanations. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4782–4793.
  38. Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics.
  39. " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144.
  40. Brad Ridout and Andrew Campbell. 2018. The use of social networking sites in mental health interventions for young people: systematic review. Journal of medical Internet research, 20(12):e12244.
  41. Predicting early psychiatric readmission with natural language processing of narrative discharge summaries. Translational psychiatry, 6(10):e921–e921.
  42. Comparison of machine learning for sentiment analysis in detecting anxiety based on social media data. arXiv preprint arXiv:2101.06353.
  43. Predicting depression and anxiety on reddit: a multi-task learning approach. In 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 427–435. IEEE.
  44. Exploring and learning suicidal ideation connotations on social media with deep learning. In Proceedings of the 9th workshop on computational approaches to subjectivity, sentiment and social media analysis, pages 167–175.
  45. Lloyd S Shapley et al. 1953. A value for n-person games.
  46. Judy Hanwen Shen and Frank Rudzicz. 2017. Detecting anxiety through reddit. In Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology—From Linguistic Signal to Clinical Reality, pages 58–65.
  47. Detection of suicide ideation in social media forums using deep learning. Algorithms, 13(1):7.
  48. Recognizing depression from twitter activity. In Proceedings of the 33rd annual ACM conference on human factors in computing systems, pages 3187–3196.
  49. Revisiting attention weights as explanations from an information theoretic perspective. In NeurIPS’22 Workshop on All Things Attention: Bridging Different Perspectives on Attention.
  50. Leveraging collaborative-filtering for personalized behavior modeling: a case study of depression detection among college students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(1):1–27.
  51. Semi-supervised approach to monitoring clinical depressive symptoms in social media. In Proceedings of the 2017 IEEE/ACM International Conference on advances in Social Networks Analysis and Mining 2017, pages 1191–1198.
  52. Prototype completion with primitive knowledge for few-shot learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3754–3762.
  53. Mam: A metaphor-based approach for mental illness detection. In International Conference on Computational Science, pages 570–583. Springer.
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Authors (4)
  1. Mohammad Saeid Mahdavinejad (6 papers)
  2. Peyman Adibi (7 papers)
  3. Amirhassan Monadjemi (5 papers)
  4. Pascal Hitzler (41 papers)

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