Skip to main content

Advertisement

SpringerLink
Log in

Approaching what and how people with mental disorders communicate in social media–Introducing a multi-channel representation

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Over the last few years, studies related to the detection of mental disorders in social media have been increasing. The latter because the awareness created by health campaigns that emphasizes the commonness of these disorders among all of us has motivated the creation of new datasets, many of them extracted from social media platforms. In this study, we aim to contribute to the analysis of three major mental disorders that are hitting the world: Anorexia, Depression and Self-harm. To this end, we propose a novel model that, first, extracts three different views, or information channels, from the posts shared by users: thematic interests, writing style, and emotions. Then, it optimally fusions the information from each channel by using a gated multimodal unit. We evaluate the feasibility of our approach in the aforementioned tasks, first by comparing its output against traditional and modern strategies, and later against the best contestants in the eRisk evaluation forum. In both evaluations, our approach clearly outperforms all of its competitors. Through an exhaustive analysis section, we provide evidence of what is being captured by each information channel, then highlighting the importance and robustness of a more holistic view in critical classification tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The data that support the findings of this study are available from https://erisk.irlab.org/. Restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of eRisk organizers.

Notes

  1. We also evaluated a Recurrent Neural Network (RNN) with an attention mechanism to learn the relation between the channels, but experiments showed a better performance for the CNN alone. We discuss more of this in the analysis of the results section.

  2. To clarify this point, our approach focuses on a binary classification task, i.e., to discriminate between users suffering from depression and control users, while, on the other hand, the eRisk task considered the assessment of the level of depression severity for each user.

References

  1. Mathers C, Loncar D (2006) Projections of global mortality and burden of disease from 2002 to 2030. Public library of science, PLOS Medicine

    Book  Google Scholar 

  2. Kessler R, Bromet E, Jonge P, Shahly V, and Marsha (2017) The burden of depressive illness. Public health perspectives on depressive disorders 40–66

  3. Guardian News and Media (2019) Mental illness: Is there really a global epidemic? The guardian

  4. Renteria-Rodriguez M (2018) Salud mental en mexico. NOTA-INCyTU NÚMERO 007

  5. Guntuku SC, Yaden D, Kern M, Ungar L, Eichstaedt J (2017) Detecting depression and mental illness on social media: an integrative review. current opin behavioral Sci 18:43–49

    Article  Google Scholar 

  6. Pestian JP, Nasrallah H, Matykiewicz P, Bennett A, and Leenaars AA (2010) Suicide note classification using natural language processing: a content analysislin heidelberg. biomed inform insights

  7. Qianli MA, Lifeng S, Enhuan C, Shuai T, Jiabing W, and Garrison C (2017) Walking walking walking: action recognition from action echoes. Twenty-Sixth International Joint Conference on Artificial Intelligence

  8. Aragón M., López-Monroy AP, González-Gurrola LC, and Montes-y Gómez M (2019) Detecting depression in social media using fine-grained emotions. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, Vol 1 (Long and Short Papers)

  9. Pennington J, Socher R, and Manning C (2014) GloVe: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)

  10. Devlin J, Chang M, Lee K, and Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. NAACL

  11. De Choudhury M, Gamon M, Counts S, and Horvitz E (2003) Predicting depression via social media. In: Proceedings of the 7th international AAAI conference on weblogs and social media

  12. De Choudhury Munmun, Counts Scott, and Horvitz Eric (2013) Social media as a measurement tool of depression in populations. In :Proceedings of the 5th annual ACM web science conference

  13. Wang Tao, Brede Markus, Ianni Antonella, and Mentzakis Emmanouil (2017) Detecting and characterizing eating-disorder communities on social media. In: Proceedings of the tenth ACM international conference on web search and data mining

  14. Tsugawa S, Kikuchi Y, Kishino F, Nakajima K, Itoh Y, and Ohsaki H (2015) Recognizing depression from twitter activity. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, pp 3187–3196

  15. Schwartz HA, Eichstaedt J, Kern M, Park G, Sap M, Stillwell D, Kosinski M, and Ungar L (2014) Towards assessing changes in degree of depression through facebook. In: Proceedings of the workshop on computational linguistics and clinical psychology: From linguistic signal to clinical reality

  16. Liu N, Zhou Z, Xin K, and Ren F (2018) Tua1 at erisk.In: Proceedings of the 9th international conference of the CLEF association, CLEF 2018, Avignon, France

  17. Coppersmith G, Harman C, and Dredze M (2014) Measuring post traumatic stress disorder in twitter. In: Proceedings of the Eighth international AAAI conference on weblogs and social media

  18. Trifan A, and Oliveira JL (2019) Bioinfo@uavr at erisk 2019: delving into social media texts for the early detection of mental and food disorders. In: Proceedings of the 10th international conference of the CLEF association, CLEF 2019, Lugano, Switzerland

  19. Van Rijen P, Teodoro D, Naderi N, Mottin L, Knafou J, Jeffryes M, and Ruch P (2019) A data-driven approach for measuring the severity of the signs of depression using reddit posts. In: Proceedings of the 10th international conference of the CLEF association, CLEF 2019, Lugano, Switzerland

  20. Ramírez-Cifuentes D, and Freire A (2018) Upf’s participation at the clef erisk 2018: Early risk prediction on the internet. In: Proceedings of the 9th international conference of the CLEF association, CLEF 2018, Avignon, France

  21. Preotiuc-Pietro D, Eichstaedt J, Park G, Sap M, Smith L, Tobolsky V, Schwartz HA, and Ungar L (2015) The role of personality, age and gender in tweeting about mental illnesses. In: Proceedings of the 2nd workshop on computational linguistics and clinical psychology

  22. American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders (5th ed.). American psychiatric association

  23. Xuetong C, Martin DS, Thomas WJ, and Suzanne E (2018) What about mood swings? identifying depression on twitter with temporal measures of emotions. Companion proceedings of the the web conference 2018, international world wide web conferences steering committee, 1653–1660

  24. Coopersmith G, Dredze M, and Harman C (2014) Quantifying mental health signals in twitter. workshop on computational linguistics and clinical psychology

  25. Coppersmith G, Ngo K, Leary R, and Wood A (2016) Exploratory analysis of social media prior to a suicide attempt. In: Proceedings of the third workshop on computational linguistics and clinical psychology

  26. Tausczik YR, Pennebaker JW (2010) The psychological meaning of words: Liwc and computerized text analysis methods. J Language Soc Psychol 29:24–54

    Article  Google Scholar 

  27. Coppersmith G, Dredze M, Harman C, and Hollingshead K (2015) 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

  28. Trotzek M, Koitka S, and Friedrich CM (2018) Word embeddings and linguistic metadata at the clef 2018 tasks for early detection of depression and anorexia. In: Proceedings of the 9th international conference of the CLEF association, CLEF 2018, Avignon, France

  29. Losada DE, Crestani F, and Parapar J (2018) Overview of erisk 2018: Early risk prediction on the internet (extended lab overview). In: Proceedings of the 9th international conference of the CLEF association, CLEF 2018, Avignon, France

  30. Mohammadi E, Amini H, and Kosseim L (2019) Quick and (maybe not so) easy detection of anorexia in social media posts. Experimental IR Meets Multilinguality, Multimodality, and Interaction. 10th International conference of the CLEF association, CLEF 2019, Lugano, Switzerland

  31. Ragheb W, Aze J, Bringay S, and Servajean M (2019) Attentive multi-stage learning for early risk detection of signs of anorexia and self-harm on social media. In: Proceedings of the 10th international conference of the CLEF association, CLEF 2019, Lugano, Switzerland

  32. Ji S, Li X, Huang Z, and Cambria E (2020) Suicidal ideation and mental disorder detection with attentive relation networks. arXiv:2004.07601

  33. Ríssola E, and Aliannejadi M, and Crestani F (2020) Beyond modelling: Understanding mental disorders in online social media. Advances in Information Retrieval: 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal

  34. Burdisso S, Errecalde M, Montes-y Gómez M (2019) A text classification framework for simple and effective early depression detection over social media streams. Expert Syst Appl 133:182–197

    Article  Google Scholar 

  35. Mohammad SM, Turney PD (2013) Crowdsourcing a word-emotion association lexicon. Comput Intell 29:436–465

    Article  MathSciNet  Google Scholar 

  36. Bojanowski P, Grave E, Joulin A, and Mikolov T (2016) Enriching word vectors with subword information. Transactions of the association for computational linguistics

  37. Arevalo J, Solorio T, Montes-y Gómez M, González FA (2019) Gated multimodal networks. Neural Comput Appl 32(14):10209–10228

    Article  Google Scholar 

  38. Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)

  39. Losada DE, Crestani F, and Parapar J (2019) Overview of erisk 2019: Early risk prediction on the internet. Experimental IR Meets Multilinguality, Multimodality, and Interaction. 10th international conference of the CLEF association, CLEF 2019, Lugano, Switzerland

  40. Losada DE, Crestani F, and Parapar J (2020) Overview of eRisk 2020: Early Risk Prediction on the Internet. Experimental IR Meets Multilinguality, Multimodality, and Interaction proceedings of the Eleventh International conference of the CLEF association (CLEF 2020)

  41. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J (1961) An inventory for measuring depression. JAMA Psychiatry 4(6):561–571

    Google Scholar 

  42. Li J, Chen X, Hovy EH, and Jurafsky D (2016) Visualizing and understanding neural models in nlp. HLT-NAACL

Download references

Acknowledgements

Aragon thanks for doctoral scholarship CONACyT-Mexico 654803.

Author information

Authors and Affiliations

  1. National Institute of Astrophysics Optics and Electronics(INAOE), Puebla, Mexico

    Mario Ezra Aragón & Manuel Montes-y-Gómez

  2. Mathematics Research Center (CIMAT), Guanajuato, Mexico

    A. Pastor López-Monroy

  3. School of Engineering, Autonomous University of Chihuahua, Chihuahua, Mexico

    Luis C. González

Authors
  1. Mario Ezra Aragón
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Pastor López-Monroy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luis C. González
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Manuel Montes-y-Gómez
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MEA helped in conceptualization, methodology, investigation, formal analysis, writing–original draft preparation. APLM contributed to methodology, validation, investigation, writing–review and editing. LCGG contributed to supervision, visualization, writing–review and editing. MMG helped in conceptualization, supervision, project administration, resources, writing–review and editing.

Corresponding author

Correspondence to Mario Ezra Aragón.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aragón, M.E., López-Monroy, A.P., González, L.C. et al. Approaching what and how people with mental disorders communicate in social media–Introducing a multi-channel representation. Neural Comput & Applic 34, 20149–20164 (2022). https://doi.org/10.1007/s00521-022-07569-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07569-8

Keywords

Search

Navigation