The language of mental health problems in social media

Abstract

Online social media, such as Reddit, has become an important resource to share personal experiences and communicate with others. Among other personal information, some social media users communicate about mental health problems they are experiencing, with the intention of getting advice, support or empathy from other users. Here, we investigate the language of Reddit posts specific to mental health, to define linguistic characteristics that could be helpful for further applications. The latter include attempting to identify posts that need urgent attention due to their nature, e.g. when someone announces their intentions of ending their life by suicide or harming others. Our results show that there are a variety of linguistic features that are discriminative across mental health user communities and that can be further exploited in subsequent classification tasks. Furthermore, while negative sentiment is almost uniformly expressed across the entire data set, we demonstrate that there are also condition-specific vocabularies used in social media to communicate about particular disorders. Source code and related materials are available from: https: //github.com/gkotsis/ reddit-mental-health.

Characterisation of mental health conditions in social media using Informed Deep Learning

“…Following on from our initial study8, here we present the second phase of our research on classifying user-generated content from Reddit that is related to different types of mental health conditions. We aimed to study the most epidemiologically prevalent and clinically burdensome mental health conditions: depression, bipolar disorder, anxiety disorders, schizophrenia, as described in the study by Whiteford et al 1…”

Monitoring Tweets for Depression to Detect At-risk Users

“…Resnik et al (2015a) proved that such approaches can be successfully used in identifying users with depression, who have self-disclosed their mental illnesses on Twitter. In general, a clear distinction in the lexical and syntactic structure of the language used by individuals with different mental disorders, as well as between individuals within a control group, can be identified throughout the literature mentioned above, as well as from the explorative analysis conducted by Gkotsis et al (2016). Due to the reliability of the lexical and behavioral features used in many of the models mentioned above, our proposed solution also focused on these feature categories.…”

Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

“…Most previous work in text classification have used various classifiers (most commonly, Support Vector Machines (SVMs) (Cortes and Vapnik, 1995) relying on different sets of features such as: constructed statistics (e.g., bag-of-words (word counts)), lexical TF-IDF, Latent Dirichlet Allocation (LDA) topics Rumshisky et al, 2016)), various linguistic and metadata features (Gkotsis et al, 2016;Bullard et al, 2016).…”

“…Understanding and identifying mental health conditions in social media (e.g., Twitter and Reddit) has been widely studied Coppersmith et al, 2014b;De Choudhury and De, 2014;Mitchell et al, 2015;Gkotsis et al, 2016;. To obtain ground truth knowledge for mental health conditions, researchers have used crowdsourced surveys and heuristics such as self-disclosure of a diagnosis Tsugawa et al, 2015).…”