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Conceptualizing Suicidal Behavior: Utilizing Explanations of Predicted Outcomes to Analyze Longitudinal Social Media Data (2312.08299v2)

Published 13 Dec 2023 in cs.CL, cs.CY, and cs.SI

Abstract: The COVID-19 pandemic has escalated mental health crises worldwide, with social isolation and economic instability contributing to a rise in suicidal behavior. Suicide can result from social factors such as shame, abuse, abandonment, and mental health conditions like depression, Post-Traumatic Stress Disorder (PTSD), Attention-Deficit/Hyperactivity Disorder (ADHD), anxiety disorders, and bipolar disorders. As these conditions develop, signs of suicidal ideation may manifest in social media interactions. Analyzing social media data using AI techniques can help identify patterns of suicidal behavior, providing invaluable insights for suicide prevention agencies, professionals, and broader community awareness initiatives. Machine learning algorithms for this purpose require large volumes of accurately labeled data. Previous research has not fully explored the potential of incorporating explanations in analyzing and labeling longitudinal social media data. In this study, we employed a model explanation method, Layer Integrated Gradients, on top of a fine-tuned state-of-the-art LLM, to assign each token from Reddit users' posts an attribution score for predicting suicidal ideation. By extracting and analyzing attributions of tokens from the data, we propose a methodology for preliminary screening of social media posts for suicidal ideation without using LLMs during inference.

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References (35)
  1. J. Liu, M. Shi, and H. Jiang, “Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion,” Int J Environ Res Public Health, vol. 19, no. 13, Jul 2022, [PubMed Central:PMC4380222] [DOI:10.1016/j.eswa.2016.06.005] [PubMed:26915165].
  2. O. Araque, I. Corcuera-Platas, J. F. Sánchez-Rada, and C. A. Iglesias, “Enhancing deep learning sentiment analysis with ensemble techniques in social applications,” Expert Systems with Applications, vol. 77, pp. 236–246, 2017. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417417300751
  3. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
  4. S. Alaparthi and M. Mishra, “Bidirectional encoder representations from transformers (bert): A sentiment analysis odyssey,” arXiv preprint arXiv:2007.01127, 2020.
  5. S. Gururangan, A. Marasović, S. Swayamdipta, K. Lo, I. Beltagy, D. Downey, and N. A. Smith, “Don’t stop pretraining: Adapt language models to domains and tasks,” arXiv preprint arXiv:2004.10964, 2020.
  6. S. Ji, T. Zhang, K. Yang, S. Ananiadou, E. Cambria, and J. Tiedemann, “Domain-specific continued pretraining of language models for capturing long context in mental health,” arXiv preprint arXiv:2304.10447, 2023.
  7. S. Ji, T. Zhang, L. Ansari, J. Fu, P. Tiwari, and E. Cambria, “Mentalbert: Publicly available pretrained language models for mental healthcare,” arXiv preprint arXiv:2110.15621, 2021.
  8. M. Sundararajan, A. Taly, and Q. Yan, “Axiomatic attribution for deep networks,” in International conference on machine learning.   PMLR, 2017, pp. 3319–3328.
  9. Y. Zhang, A. Khakzar, Y. Li, A. Farshad, S. T. Kim, and N. Navab, “Fine-grained neural network explanation by identifying input features with predictive information,” Advances in Neural Information Processing Systems, vol. 34, pp. 20 040–20 051, 2021.
  10. Y. Hao, L. Dong, F. Wei, and K. Xu, “Self-attention attribution: Interpreting information interactions inside transformer,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 14, 2021, pp. 12 963–12 971.
  11. A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Žídek, A. W. Nelson, A. Bridgland et al., “Improved protein structure prediction using potentials from deep learning,” Nature, vol. 577, no. 7792, pp. 706–710, 2020.
  12. W. Chen, X. Yuan, S. Zhang, J. Wu, Y. Zhang, and Y. Wang, “Ferryman at semeval-2020 task 3: bert with tfidf-weighting for predicting the effect of context in word similarity,” in Proceedings of the fourteenth workshop on semantic evaluation, 2020, pp. 281–285.
  13. M. De Choudhury, S. Counts, and E. Horvitz, “Predicting postpartum changes in emotion and behavior via social media,” in Proceedings of the SIGCHI conference on human factors in computing systems, 2013, pp. 3267–3276.
  14. G. Coppersmith, M. Dredze, C. Harman, and K. Hollingshead, “From adhd to sad: Analyzing the language of mental health on twitter through self-reported diagnoses,” in Proceedings of the 2nd workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality, 2015, pp. 1–10.
  15. S. C. Guntuku, D. B. Yaden, M. L. Kern, L. H. Ungar, and J. C. Eichstaedt, “Detecting depression and mental illness on social media: an integrative review,” Current Opinion in Behavioral Sciences, vol. 18, pp. 43–49, 2017.
  16. C. Saxena, M. Garg, and G. Ansari, “Explainable causal analysis of mental health on social media data,” in International Conference on Neural Information Processing.   Springer, 2022, pp. 172–183.
  17. W. Zhang, J. Xie, X. Liu, and Z. Zhang, “Depression detection using digital traces on social media: A knowledge-aware deep learning approach,” arXiv e-prints, pp. arXiv–2303, 2023.
  18. B. Lamichhane, “Evaluation of chatgpt for nlp-based mental health applications,” arXiv preprint arXiv:2303.15727, 2023.
  19. S. Sarkar, A. Alhamadani, L. Alkulaib, and C.-T. Lu, “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).   IEEE, 2022, pp. 427–435.
  20. M. Nagahisarchoghaei, N. Nur, L. Cummins, N. Nur, M. M. Karimi, S. Nandanwar, S. Bhattacharyya, and S. Rahimi, “An empirical survey on explainable ai technologies: Recent trends, use-cases, and categories from technical and application perspectives,” Electronics, vol. 12, no. 5, p. 1092, 2023.
  21. S. K. Ernala, A. F. Rizvi, M. L. Birnbaum, J. M. Kane, and M. De Choudhury, “Linguistic markers indicating therapeutic outcomes of social media disclosures of schizophrenia,” Proceedings of the ACM on Human-Computer Interaction, vol. 1, no. CSCW, pp. 1–27, 2017.
  22. S. Chancellor and M. De Choudhury, “Methods in predictive techniques for mental health status on social media: a critical review,” NPJ digital medicine, vol. 3, no. 1, p. 43, 2020.
  23. M. Calloway, J. P. Morrissey, and R. I. Paulson, “Accuracy and reliability of self-reported data in interorganizational networks,” Social Networks, vol. 15, no. 4, pp. 377–398, 1993.
  24. M. De Choudhury and E. Kiciman, “The language of social support in social media and its effect on suicidal ideation risk,” in Proceedings of the international AAAI conference on web and social media, vol. 11, no. 1, 2017, pp. 32–41.
  25. I. Beltagy, M. E. Peters, and A. Cohan, “Longformer: The long-document transformer,” 2020.
  26. A. Zirikly, P. Resnik, Ö. Uzuner, and K. Hollingshead, “CLPsych 2019 shared task: Predicting the degree of suicide risk in Reddit posts,” in Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, June 2019.
  27. P. He, J. Gao, and W. Chen, “Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing,” 2023.
  28. A. Conneau, K. Khandelwal, N. Goyal, V. Chaudhary, G. Wenzek, F. Guzmán, E. Grave, M. Ott, L. Zettlemoyer, and V. Stoyanov, “Unsupervised cross-lingual representation learning at scale,” 2020.
  29. M. Schuster and K. Nakajima, “Japanese and korean voice search,” in International Conference on Acoustics, Speech and Signal Processing, 2012, pp. 5149–5152.
  30. Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, J. Klingner, A. Shah, M. Johnson, X. Liu, Łukasz Kaiser, S. Gouws, Y. Kato, T. Kudo, H. Kazawa, K. Stevens, G. Kurian, N. Patil, W. Wang, C. Young, J. Smith, J. Riesa, A. Rudnick, O. Vinyals, G. Corrado, M. Hughes, and J. Dean, “Google’s neural machine translation system: Bridging the gap between human and machine translation,” 2016.
  31. Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “Roberta: A robustly optimized bert pretraining approach,” 2019.
  32. R. Sennrich, B. Haddow, and A. Birch, “Neural machine translation of rare words with subword units,” 2016.
  33. Z. Wang, P. Ng, X. Ma, R. Nallapati, and B. Xiang, “Multi-passage bert: A globally normalized bert model for open-domain question answering,” 2019.
  34. H. Chua, A. Caines, and H. Yannakoudakis, “A unified framework for cross-domain and cross-task learning of mental health conditions,” in Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI).   Abu Dhabi, United Arab Emirates (Hybrid): Association for Computational Linguistics, Dec. 2022, pp. 1–14. [Online]. Available: https://aclanthology.org/2022.nlp4pi-1.1
  35. A. Lee, J. K. Kummerfeld, L. C. An, and R. Mihalcea, “Micromodels for efficient, explainable, and reusable systems: A case study on mental health,” 2021.
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Authors (8)
  1. Van Minh Nguyen (19 papers)
  2. Nasheen Nur (2 papers)
  3. William Stern (1 paper)
  4. Thomas Mercer (1 paper)
  5. Chiradeep Sen (2 papers)
  6. Siddhartha Bhattacharyya (26 papers)
  7. Victor Tumbiolo (1 paper)
  8. Seng Jhing Goh (1 paper)
Citations (2)
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