Automatic Detection and Classification of Mental Illnesses from General Social Media Texts

Proceedings of Recent Advances in Natural Language Processing, pages 358–366 Sep 1–3, 2021. https://doi.org/10.26615/978-954-452-072-4_041 358 Abstract Mental health is getting more and more attention recently, depression being a very common illness nowadays, but also other disorders like anxiety, obsessive-compulsive disorders, feeding disorders, autism, or attention-deficit/hyperactivity disorders. The huge amount of data from social media and the recent advances of deep learning models provide valuable means to automatically detecting mental disorders from plain text. In this article, we experiment with state-of-the-art methods on the SMHD mental health conditions dataset from Reddit (Cohan et al., 2018). Our contribution is threefold: using a dataset consisting of more illnesses than most studies, focusing on general text rather than mental health support groups and classification by posts rather than individuals or groups. For the automatic classification of the diseases, we employ three deep learning models: BERT, RoBERTa and XLNET. We double the baseline established by Cohan et al. (2018), on just a sample of their dataset. We improve the results obtained by Jiang et al. (2020) on post-level classification. The accuracy obtained by the eating disorder classifier is the highest due to the pregnant presence of discussions related to calories, diets, recipes etc., whereas depression had the lowest F1 score, probably because depression is more difficult to identify in linguistic acts. 1 Introduction An analysis performed by Chisholm et al. (2016) estimates that approximately 10% of the world’s population is living with a mental illness. The Global Burden of Disease Study (2017) states that depression is a very common illness and there are more than 264 million people affected by it. At its worst, the illness can lead to suicide and it is the second highest cause of death for people between 15 and 29. Between 76% and 85% of the potentially diagnosed people, do not benefit from any treatment for their illness due to living in impoverished areas and not having access to mental care. It is difficult to discuss about digital solutions in the context of isolated areas with low data availability and limited access to professional help. Social stigma is another obstacle present regardless of age, gender or race, which makes early intervention difficult. Persons facing difficulties often avoid discussing their issues from various reasons. However, researchers working with Machine Learning algorithms can draw plenty of expertise from the unstructured data roaming the World Wide Web. The advent of social media platforms brings up an influx of large quantities of various types of unstructured textual data. The continuous advancements made in the field of Machine Learning enable the possibility to analyse such volumes of data efficiently. Experiments in this interdisciplinary domain managed to bring up useful input for mental health practitioners, socio- linguists, computer scientist and other researchers in the field. Pennebaker et al. (2015) perform one of the most influential quantitative studies, which reveals the way patterns of parts of speech, as labelled by LIWC founders, correlate with types of personalities and types of mental illnesses. The classes and the psychological …

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