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Suicidal ideation and mental disorder detection with attentive relation networks

  • S. I.: Effective and Efficient Deep Learning
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Abstract

Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment. Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention. However, classifying suicidal ideation and other mental disorders is challenging as they share similar patterns in language usage and sentimental polarity. This paper enhances text representation with lexicon-based sentiment scores and latent topics and proposes using relation networks to detect suicidal ideation and mental disorders with related risk indicators. The relation module is further equipped with the attention mechanism to prioritize more critical relational features. Through experiments on three real-world datasets, our model outperforms most of its counterparts.

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Data availability

The authors would like to thank Philip Resnik for providing the UMD Reddit Suicidality Dataset (available via http://users.umiacs.umd.edu/~resnik/umd_reddit_suicidality_dataset.html.) and Mark Dredze for providing the dataset (available via http://www.cs.jhu.edu/~mdredze/datasets/clpsych_shared_task_2015/.) in the CLPsych 2015 shared task. This research is supported by the Australian Research Council (ARC) Linkage Project (LP150100671).

Notes

  1. Mental health action plan 2013–2020, available in http://www.who.int/mental_health/action_plan_2013/mhap_brochure.pdf?ua=1.

  2. NAMI report on Risk Of Suicide, available at https://www.nami.org/Learn-More/Mental-Health-Conditions/Related-Conditions/Suicide.

  3. Records updated on Apr 02, 2020. Available via https://www.worldbank.org/en/topic/mental-health.

  4. Published online at OurWorldInData.org. Retrieved from https://ourworldindata.org/mental-health.

  5. http://psychiatry.org/psychiatrists/practice/dsm.

  6. http://apps.who.int/classifications/icd10/browse/2016/en#/V.

  7. http://radimrehurek.com/gensim.

  8. http://reddit.com/r/SuicideWatch.

  9. http://reddit.com/dev/api.

  10. Request for data access via http://www.cs.jhu.edu/~mdredze/datasets/clpsych_shared_task_2015/.

  11. http://liwc.wpengine.com.

  12. http://pytorch.org.

References

  1. Windfuhr K, Kapur N (2011) Suicide and mental illness: a clinical review of 15 years findings from the uk national confidential inquiry into suicide. Br Med Bull 100(1):101–121

    Article  Google Scholar 

  2. De Choudhury M, Kiciman E, Dredze M, Coppersmith G, Kumar M (2016) Discovering shifts to suicidal ideation from mental health content in social media. In CHI, pages 2098–2110. ACM

  3. Jacob KS, Patel V (2014) Classification of mental disorders: a global mental health perspective. Lancet 383(9926):1433–1435

    Article  Google Scholar 

  4. Gilat I, Tobin Y, Shahar G (2011) Offering support to suicidal individuals in an online support group. Arch Suicide Res 15(3):195–206

    Article  Google Scholar 

  5. Shepherd A, Sanders C, Doyle M, Shaw J (2015) Using social media for support and feedback by mental health service users: thematic analysis of a twitter conversation. BMC Psychiat 15(1):29

    Article  Google Scholar 

  6. Ji S, Pan S, Li X, Cambria E, Long G, Huang Z (2021) Suicidal ideation detection: a review of machine learning methods and applications. IEEE Trans Comput Soc Syst 8(1):214–226

    Article  Google Scholar 

  7. Coppersmith G, Leary R, Whyne E, Wood T (2015) Quantifying suicidal ideation via language usage on social media. In Joint Statistics Meetings Proceedings, Statistical Computing Section, JSM

  8. Ji S, Yu CP, Fung SF, Pan S, Long G (2018) Supervised learning for suicidal ideation detection in online user content. Complexity. https://doi.org/10.1155/2018/6157249

    Article  Google Scholar 

  9. Chen X, Sykora MD, Jackson TW, Elayan S (2018) What about mood swings: Identifying depression on twitter with temporal measures of emotions. In The Web Conference, pages 1653–1660

  10. Wang Z, Ho S, Cambria E (2020) A review of emotion sensing: categorization models and algorithms. Multimed Tools Appl 79:35553–35582

    Article  Google Scholar 

  11. McCart JA, Finch DK, Jarman J, Hickling E, Lind JD, Richardson MR, Berndt DJ, Luther SL (2012) Using ensemble models to classify the sentiment expressed in suicide notes. Biomedical informatics insights, 5: BII–S8931

  12. Raposo D, Santoro A, Barrett D, Pascanu R, Lillicrap T, Battaglia P (2017) Discovering objects and their relations from entangled scene representations. arXiv preprint arXiv:1702.05068

  13. Venek V, Scherer S, Morency LP, Pestian J (2017) Adolescent suicidal risk assessment in clinician-patient interaction. IEEE Trans Aff Comput 8(2):204–215

    Article  Google Scholar 

  14. Delgado-Gomez D, Blasco-Fontecilla H, Sukno F, Ramos-Plasencia MS, Baca-Garcia E (2012) Suicide attempters classification. Toward predictive models of suicidal behavior. Neurocomputing 92:3–8

    Article  Google Scholar 

  15. Benton A, Mitchell M, Hovy D (2017) Multi-task learning for mental health using social media text. In EACL, ACL

  16. Ji S, Long G, Pan S, Zhu T, Jiang J, Wang S (2019) Detecting suicidal ideation with data protection in online communities. DASFAA. Springer, Cham, pp 225–229

    Google Scholar 

  17. Pavalanathan U, De Choudhury M (2015) Identity management and mental health discourse in social media. In WWW, pages 315–321. ACM

  18. Wang Y, Tang J, Li J, Li B, Wan Y, Mellina C, O’Hare N, Chang Y (2017) Understanding and discovering deliberate self-harm content in social media. In WWW, pages 93–102

  19. Shuai HH, Shen CY, Yang DN, Lan YF, Lee WC, Yu PS, Chen MS (2016) Mining online social data for detecting social network mental disorders. In WWW, pages 275–285

  20. De Choudhury M, Gamon M, Counts S, Horvitz E (2013) Predicting depression via social media. In AAAI ICWSM

  21. Li Y, Mihalcea R, Wilson SR (2018) Text-based detection and understanding of changes in mental health. In International Conference on Social Informatics, pages 176–188. Springer

  22. Cao L, Zhang H, Feng L (2020) Building and using personal knowledge graph to improve suicidal ideation detection on social media. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2020.3046867

    Article  Google Scholar 

  23. Nguyen T, Phung D, Dao B, Venkatesh S, Berk M (2014) Affective and content analysis of online depression communities. IEEE Trans Aff Comput 5(3):217–226

    Article  Google Scholar 

  24. Ren F, Kang X, Quan C (2016) Examining accumulated emotional traits in suicide blogs with an emotion topic model. IEEE J Biomed Health Inform 20(5):1384–1396

    Article  Google Scholar 

  25. Minaee S, Kalchbrenner N, Cambria E, Nikzad N, Chenaghlu M, Gao J (2021) Deep learning based text classification: A comprehensive review. ACM Computing Surveys 54

  26. Kim Y (2014) Convolutional neural networks for sentence classification. In EMNLP, pages 1746–1751

  27. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  28. Lai S, Xu L, Liu K, Zhao J (2015) Recurrent convolutional neural networks for text classification. AAAI 333:2267–2273

    Google Scholar 

  29. Li Y, Pan Q, Wang S, Yang T, Cambria E (2018) A generative model for category text generation. Inf Sci 450:301–315

    Article  MathSciNet  Google Scholar 

  30. Zhao W, Peng H, Eger S, Cambria E, Yang M (2019) Towards scalable and reliable capsule networks for challenging NLP applications. In ACL, pages 1549–1559

  31. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473

  32. Lin Z, Feng M, Santos CND, Yu M, Xiang B, Zhou B, Bengio Y (2017) A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130

  33. Ma Y, Peng H, Cambria E (2018) Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In AAAI, pages 5876–5883

  34. Hamilton WL, Clark K, Leskovec J, Jurafsky D (2016) Inducing domain-specific sentiment lexicons from unlabeled corpora. In EMNLP, volume 2016, page 595

  35. Santoro A, Raposo D, Barrett DG, Malinowski M, Pascanu R, Battaglia P, Lillicrap T (2017) A simple neural network module for relational reasoning. In NIPS, pages 4967–4976

  36. Cambria E, Wang H, White B (2014) Guest editorial: big social data analysis. Know-Based Syst 69:1–2

    Article  Google Scholar 

  37. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  38. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

  39. Shing HC, Nair S, Zirikly A, Friedenberg M, Daumé III H, Resnik P (2018) Expert, crowdsourced, and machine assessment of suicide risk via online postings. In Proceedings of the Fifth Workshop on CLPsych, pages 25–36

  40. Zirikly A, Resnik P, Uzuner Ö, Hollingshead K (2019) CLPsych 2019 shared task: Predicting the degree of suicide risk in Reddit posts. In Proceedings of the Sixth Workshop on CLPsych

  41. Coppersmith G, Dredze M, Harman C, Hollingshead K, Mitchell M (2015) Clpsych 2015 shared task: Depression and ptsd on twitter. In Proceedings of the 2nd Workshop on CLPsych, pages 31–39

  42. Joulin A, Grave E, Bojanowski P, Mikolov T (2016) Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759

  43. Pennington J, Socher R, Manning C (2014) GloVe: Global vectors for word representation. In EMNLP, pages 1532–1543

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Author information

Authors and Affiliations

  1. Department of Computer Science, Aalto University, Espoo, Finland

    Shaoxiong Ji

  2. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia

    Xue Li & Zi Huang

  3. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Erik Cambria

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  1. Shaoxiong Ji
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  2. Xue Li
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  3. Zi Huang
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  4. Erik Cambria
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Correspondence to Erik Cambria.

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S. Ji: The work was done while this author was at the University of Queensland.

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Ji, S., Li, X., Huang, Z. et al. Suicidal ideation and mental disorder detection with attentive relation networks. Neural Comput & Applic 34, 10309–10319 (2022). https://doi.org/10.1007/s00521-021-06208-y

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  • DOI: https://doi.org/10.1007/s00521-021-06208-y

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