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On the Explainability of Automatic Predictions of Mental Disorders from Social Media Data

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Natural Language Processing and Information Systems (NLDB 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12801))

Abstract

Mental disorders are an important public health issue, and computational methods have the potential to aid with detection of risky behaviors online, through extracting information from social media in order to retrieve users at risk of developing mental disorders. At the same time, state-of-the-art machine learning models are based on neural networks, which are notoriously difficult to interpret. Exploring the explainability of neural network models for mental disorder detection can make their decisions more reliable and easier to trust, and can help identify specific patterns in the data which are indicative of mental disorders. We aim to provide interpretations for the manifestations of mental disorder symptoms in language, as well as explain the decisions of deep learning models from multiple perspectives, going beyond classical techniques such as attention analysis, and including activation patterns in hidden layers, and error analysis focused on particular features such as the emotions and topics found in texts, from a technical as well as psycho-linguistic perspective, for different social media datasets (sourced from Reddit and Twitter), annotated for four mental disorders: depression, anorexia, PTSD and self-harm tendencies.

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Notes

  1. 1.

    https://early.irlab.org/.

  2. 2.

    https://github.com/ananana/mental-disorders.

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Acknowledgement

The authors thank the EU-FEDER Comunitat Valenciana 2014–2020 grant IDIFEDER/2018/025. The work of Paolo Rosso was in the framework of the research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana.

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Authors and Affiliations

  1. Pattern Recognition and Human Language Technology (PRHLT), Universitat Politècnica de València, Valencia, Spain

    Ana Sabina Uban, Berta Chulvi & Paolo Rosso

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  1. Ana Sabina Uban
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  2. Berta Chulvi
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  3. Paolo Rosso
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Corresponding author

Correspondence to Ana Sabina Uban .

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Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Derby, Derby, UK

    Farid Meziane

  3. German Research Center for Artificial Intelligence, Saarbrücken, Germany

    Helmut Horacek

  4. University of Hertfordshire, Hatfield, UK

    Epaminondas Kapetanios

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Uban, A.S., Chulvi, B., Rosso, P. (2021). On the Explainability of Automatic Predictions of Mental Disorders from Social Media Data. In: Métais, E., Meziane, F., Horacek, H., Kapetanios, E. (eds) Natural Language Processing and Information Systems. NLDB 2021. Lecture Notes in Computer Science(), vol 12801. Springer, Cham. https://doi.org/10.1007/978-3-030-80599-9_27

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  • DOI: https://doi.org/10.1007/978-3-030-80599-9_27

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