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SetembroBR: a social media corpus for depression and anxiety disorder prediction

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Abstract

The present work introduces a novel dataset—hereby called the SetembroBR corpus—for the study and development of depression and anxiety disorder predictive models in the Portuguese language based on the information prior to a diagnosis. The corpus comprises both text- and network-related information related to 3.9 thousand Twitter users who self-reported a diagnosis or treatment for a mental disorder, and its use is illustrated by a number of experiments addressing the issues of depression and anxiety disorder prediction from social media data. Our present results are intended as a first step towards investigating how mental health statuses are expressed on Portuguese-speaking social media, and pave the way for computational applications intended to assist with a pressing issue of great social interest.

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Notes

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

  2. Which are nevertheless possible when using questionnaires and interviews as well.

  3. Portuguese pronoun resolution has been the focus of Paraboni (1997), Paraboni and de Lima (1998) and others.

  4. https://drive.google.com/drive/folders/1MXFRs0u8iF1RNUWABTA0Oz8_Ix1skqZT?usp=sharing.

  5. The full list of original (Portuguese) query strings is available from https://drive.google.com/file/d/16eSFtUo9l1pGbpt0BDr_1ZCmNRGpw0Vn/view?usp=sharing.

  6. When two or more valid end points are available, as in a preliminary diagnosis given by a psychologist, which is confirmed later on by a psychiatrist, the earliest possible event is marked as [end].

  7. Similarly, in Yates et al. (2017), for instance, users with fewer than 100 posts are discarded.

  8. Not real Twitter data.

  9. Making 3903 partially overlapping diagnosed cases in total.

  10. Random message selection is intended to avoid making assumptions about the distribution of messages in time. More domain-dependent alternatives to this will be discussed in the subsequent sections.

  11. In a pilot study, we also attempted to use higher order n-gram models, but results remained essentially the same as those presently reported for unigrams.

  12. https://eli5.readthedocs.io/en/latest/.

  13. Recall that, in both tasks, the negative class consists of random data, and it is presently not under scrutiny.

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Acknowledgements

This work was carried out at the Center for Artificial Intelligence (C4AI-USP), with support by the São Paulo Research Foundation (FAPESP Grant #2019/07665-4) and by the IBM Corporation. The present work has been financed by the São Paulo Research Foundation (FAPESP Grant #2021/08213-0). The first author has been supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 - Grant # 88887.475847/2020-00.

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  1. School of Arts, Sciences and Humanities (EACH), University of São Paulo (USP), São Paulo, Brazil

    Wesley Ramos dos Santos, Rafael Lage de Oliveira & Ivandré Paraboni

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Appendix: Examples of self-disclosure and discarded statements

Appendix: Examples of self-disclosure and discarded statements

The following are examples of self-disclosure tweets that would be selected for further inspection in the corpus collection task.

  • Acabo de ser diagnosticado com depressão e ansiedade. I have just been diagnosed with depression and anxiety.

  • Comecei meu tratamento para ansiedade em 2017. I started my treatment for anxiety in 2017.

  • Uau adivinha quem voltou da psicóloga com uma receita de antidepressivo? Wow guess who just came back from the psychologist’s with an antidepressant prescription?

  • Fui diagnosticado com ansiedade no final do ano passado. I was diagnosed with anxiety at the end of last year.

  • Fui ao médico e recebi diagnóstico de TAG. Ainda não estou acreditando. I went to the doctor’s and I received a GAD diagnosis. I still can’t believe it

The following are examples of tweets that would be discarded during the corpus collection task.

  • (unknown diagnosis date). Fui diagnosticado com depressão aos 15 anos de idade. I was diagnosed with depression at the age of 15.

  • (recurrent condition) Comecei hoje um tratamento para depressão. Lá vamos nós outra vez! Today I started a treatment for depression. Here we go again!

  • (irony/sarcasm) Depois de assistir a esse vídeo eu fui diagnosticado com depressão profunda. After watching this video I was diagnosed with severe depression.

  • (no medical specialist, or irony/sarcasm) Acabo de ser diagnosticado com depressão de acordo com o teste do Ursinho Pooh I have just been diagnosed with depression according to a Winnie-the-Pooh test.

  • (no explicit depression/anxiety disclosure) Hoje comecei a tomar um antidepressivo para a enxaqueca. Today I started taking an antidepressant for migraine.

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Santos, W.R.d., de Oliveira, R.L. & Paraboni, I. SetembroBR: a social media corpus for depression and anxiety disorder prediction. Lang Resources & Evaluation 58, 273–300 (2024). https://doi.org/10.1007/s10579-022-09633-0

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