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A review on recognizing depression in social networks: challenges and opportunities

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Abstract

Social networks have become another resource for supporting mental health specialists in making inferences and finding indications of mental disorders, such as depression. This paper addresses the state-of-the-art regarding studies on recognition of depressive mood disorders in social networks through approaches and techniques of sentiment and emotion analysis. The systematic research conducted focused on social networks, social media, and the most employed techniques, feelings, and emotions were analyzed to find predecessors of a depressive disorder. Discussions on the research gaps identified aimed at improving the effectiveness of the analysis process, bringing the analysis close to the user’s reality. Twitter, Facebook, Blogs and Forums, Reddit, Live Journal, and Instagram are the most employed social networks regarding the identification of depressive mood disorders, and the most used information was text, followed by emoticons, user log information, and images. The selected studies usually employ classic off-the-shelf classifiers for the analysis of the available information, combined with lexicons such as NRC Word-Emoticon Association Lexicon, WordNet-Affect, Anew, and LIWC tool. The challenges include the analysis of temporal information and a combination of different types of information.

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Notes

  1. Available online in http://www.dsm5.org/Pages/Feedback-Form.aspx.

  2. Available at parsif.al.

  3. The Live Journal Platform: livejournal.com.

  4. The Psycho-Babble Grief: dr-bob.org/babble/grief/.

  5. The Russian network VKontakte.We: vk.com.

  6. IBM SPSS: ibm.com/br-pt/marketplace/spss-statistics/.

  7. https://developers.facebook.com/docs/graph-api.

  8. https://developers.facebook.com/docs/instagram-basic-display-api/.

  9. https://developer.twitter.com/en/docs.

  10. https://praw.readthedocs.io/en/latest/.

  11. https://www.mongodb.com/.

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Acknowledgements

This research was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001, the São Paulo Research Foundation (FAPESP—Grant numbers 2016/17078-0, 2018/24414-2 and 2018/17335-9), the Center of Mathematical Sciences Applied to Industry (CeMEAI, under FAPESP Grant Number 2013/07375-0), and the National Council for Scientific and Technological Development (CNPq).

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Authors and Affiliations

  1. Institute of Mathematical and Computer Sciences, University of São Paulo, São Carlos, SP, Brazil

    Felipe T. Giuntini, Mirela T. Cazzolato, Agma J. M. Traina & Jó Ueyama

  2. Department of Psychology, Federal University of São Carlos, São Carlos, SP, Brazil

    Maria de Jesus Dutra dos Reis

  3. Department of Computer Science, Dartmouth College, Hanover, NH, USA

    Andrew T. Campbell

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  1. Felipe T. Giuntini
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Correspondence to Felipe T. Giuntini.

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Giuntini, F.T., Cazzolato, M.T., dos Reis, M.d.J.D. et al. A review on recognizing depression in social networks: challenges and opportunities. J Ambient Intell Human Comput 11, 4713–4729 (2020). https://doi.org/10.1007/s12652-020-01726-4

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