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Comparison of Machine Learning Models for Early Depression Detection from Users’ Posts

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Early Detection of Mental Health Disorders by Social Media Monitoring

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1018))

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Abstract

With around 300 millions people worldwide suffering from depression, the detection of this disorder is crucial and a challenge for individual and public health. As with many diseases, early detection means better medical management; the use of social media messages as potential clues to depression is an opportunity to assist in this early detection by automatic means. This chapter is based on the participation of the CNRS IRIT laboratory in the early detection of depressive people (eRisk) task at the CLEF evaluation forum. Early depression detection differs from depression detection in that it considers temporality; the system must make its decision about a user’s possible depression with as little data as possible. In this chapter we re-evaluate the models we have developed for our participation at eRisk over the years on the different collections, to obtain a more robust comparison. We also add new models. We use well-established classification methods, such as Logistic regression, Random forest, and Support Vector Machine (SVM). The users’ data from which the system should detect if they are depressed, are represented as vectors composed of (a) various task-oriented features including depression related lexicons and (b) word and document embeddings, extracted from the users’ posts. We perform an ablation study to analyze the most important features for our models. We also use BERT deep learning architecture for comparison purposes, both for depression detection and early depression detection. According to our results, well-established machine learning models are still better than more modern models for -early- detection of depression.

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Notes

  1. 1.

    https://www.la-depression.org/, accessed January 28, 2021.

  2. 2.

    http://www.doctissimo.fr/psychologie/news/la-france-pays-le-plus-touche-par-la-depression, accessed January 28, 2021.

  3. 3.

    CES-D stands for Center for Epidemiologic Studies Depression who provides a questionnaire that can be used to detect depression [35].

  4. 4.

    Reddit is a social news aggregation, web content rating, and discussion website (https://www.reddit.com).

  5. 5.

    https://www.who.int/news-room/fact-sheets/detail/depression.

  6. 6.

    http://en.wikipedia.org/wiki/List_of_antidepressants  accessed on 23/02/2017.

  7. 7.

    http://www.webmd.com/depression/guide/depression-medications-antidepressants accessed on 10/01/2018.

  8. 8.

    http://empath.stanford.edu.

  9. 9.

    https://github.com/google-research/bert, accessed on 02/02/2021.

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Acknowledgements

This work is partially supported by the PREVISION project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under GA No 833115 (https://cordis.europa.eu/project/id/833115). The paper reflects the authors’ view and the Commission is not responsible for any use that may be made of the information it contains.

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Authors and Affiliations

  1. ESPE, Univ. Toulouse Jean-Jaurès, Univ. de Toulouse, IRIT, UMR5505 CNRS, 118 Rte de Narbonne, Toulouse, France

    Josiane Mothe

  2. Univ. de Toulouse, IRIT, UMR5505 CNRS, 118 Rte de Narbonne, Toulouse, France

    Faneva Ramiandrisoa & Md Zia Ullah

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  1. Josiane Mothe
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  2. Faneva Ramiandrisoa
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  3. Md Zia Ullah
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Correspondence to Josiane Mothe .

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Editors and Affiliations

  1. Faculty of Informatics, University of Lugano, Lugano, Switzerland

    Fabio Crestani

  2. Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    David E. Losada

  3. Centro de Investigación en Tecnoloxías da Información e as Comunicacións (CITIC), Universidade da Coruña, A Coruña, Spain

    Javier Parapar

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Mothe, J., Ramiandrisoa, F., Ullah, M.Z. (2022). Comparison of Machine Learning Models for Early Depression Detection from Users’ Posts. In: Crestani, F., Losada, D.E., Parapar, J. (eds) Early Detection of Mental Health Disorders by Social Media Monitoring. Studies in Computational Intelligence, vol 1018. Springer, Cham. https://doi.org/10.1007/978-3-031-04431-1_5

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