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Depression and anorexia detection in social media as a one-class classification problem

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Abstract

Taking advantage of the increasing amount of user-generated content in social media, some computational methods have already been proposed for detecting people suffering from depression and anorexia. Such complex tasks have been tackled as a binary classification problem using, in most cases, automatically generated training data. Despite its promising results, this approach has some important drawbacks, namely: it suffers from a severely skewed class distribution, the negative class is very diverse since it attempts to model all kinds of healthy users, and, above all, there is not a complete certainty about annotations, especially for the negative cases (i.e., healthy users). Motivated by these issues, in this paper, we propose to face the detection of these disorders following a one-class classification (OCC) approach. Particularly, we introduce two new instance-based OCC methods especially suited to manage the high diversity of content from social media documents. Taking up ideas from the gravitational attraction force, these methods evaluate the relation of documents by their strengths, considering their distances as well as their masses (relevance) with respect to the target task. Experiments were conducted on depression and anorexia benchmark datasets. The obtained results are encouraging; the overall performance was better than the results from other standard OCC methods, and competitive with regard to state-of-the-art results from binary classification approaches.

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Data Availability

The data exploited for experimental purposes have not been collected by the authors of this manuscript. We kindly refer to the original owners for obtaining them.

Notes

  1. Groups who defend eating disorders as a lifestyle, often denoted as proana.

  2. http://clpsych.org/

  3. https://early.irlab.org/

  4. This component of Formula (1) can be computed using other measures such as the Euclidean distance. Indeed, we carried out experiments with different distance measures, obtaining a better performance when the cosine distance was used.

  5. We define a personal phrase as a sentence that contains a singular first-person pronoun.

  6. For example, the words listed in: https://github.com/first20hours/google-10000-english/blob/master/google-10000-english.txt

  7. The data can be obtained upon request. More information can be found in https://early.irlab.org/

  8. https://my.clevelandclinic.org/health/articles/9285-depression-glossary-of-depression-related-terms

  9. https://urbanthesaurus.org/synonyms/anorexia

  10. https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html

  11. The DB lexicon was discarded because it is computed from the training set, and its quality highly depends on the amount of training instances.

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Funding

This research was partially supported by CONACYT: project grant FC-2016-2410, postdoctoral fellowship CVU-174410, and graduate scholarship CVU-814295.

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Authors and Affiliations

  1. Coordinación de Ciencias Computacionales, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Puebla, Mexico

    Juan Aguilera & Manuel Montes-y-Gómez

  2. División de Ciencias e Ingenierías Campus León, Universidad de Guanajuato, Guanajuato, Mexico

    Delia Irazú Hernández Farías

  3. Universidad Politécnica de Tulancingo (UPT), Hidalgo, Mexico

    Rosa María Ortega-Mendoza

Authors
  1. Juan Aguilera
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  2. Delia Irazú Hernández Farías
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  3. Rosa María Ortega-Mendoza
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  4. Manuel Montes-y-Gómez
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Correspondence to Rosa María Ortega-Mendoza.

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Aguilera, J., Farías, D.I.H., Ortega-Mendoza, R.M. et al. Depression and anorexia detection in social media as a one-class classification problem. Appl Intell 51, 6088–6103 (2021). https://doi.org/10.1007/s10489-020-02131-2

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