Recognizing Depression from Twitter Activity

Abstract

In this paper, we extensively evaluate the effectiveness of using a user's social media activities for estimating degree of depression. As ground truth data, we use the results of a web-based questionnaire for measuring degree of depression of Twitter users. We extract several features from the activity histories of Twitter users. By leveraging these features, we construct models for estimating the presence of active depression. Through experiments, we show that (1) features obtained from user activities can be used to predict depression of users with an accuracy of 69%, (2) topics of tweets estimated with a topic model are useful features, (3) approximately two months of observation data are necessary for recognizing depression, and longer observation periods do not contribute to improving the accuracy of estimation for current depression; sometimes, longer periods worsen the accuracy.

Latent sentiment topic modelling and nonparametric discovery of online mental health-related communities

“…probabilistic latent semantic indexing (pLSI) [25], latent Dirichlet allocation (LDA) [2], or hierarchical Dirichlet processes (HDP) [65]) have shown to be effective in discovering latent topics from the corpus of blog posts. Several studies [35,41,43,44,58,66] used the standard parametric model LDA to learn latent topics from the content of blogs and tweets in the blogosphere for their research on mental health signals in social media. Using LDA to gain latent topics, [41] found significant differences among study cohorts which are characterized by the latent topics of discussion, psycholinguistic features, and tagged moods.…”

What type of happiness are you looking for? - A closer look at detecting mental health from language

“…A symptom-based approach would also account for diversity of symptoms that may constitute distress. This might provide another approach address recent concerns about the external validity of depression models to culture and gender compositions of populations (De Choudhury et al, 2016;Tsugawa et al, 2015). Research using clinical texts, namely medical notes, has already begun to move in a symptom-based direction with success and may provide inspiration (Jackson et al, 2017).…”

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

“…A symptom-based approach would also account for diversity of symptoms that may constitute distress. This might provide another approach address recent concerns about the external validity of depression models to culture and gender compositions of populations Tsugawa et al, 2015). Research using clinical texts, namely medical notes, has already begun to move in a symptom-based direction with success and may provide inspiration (Jackson et al, 2017).…”

“…Given prevalence and heavy toll of depression, it may not be surprising that this mental illness the focus of modeling efforts Fraser et al, 2016;Gupta et al, 2014;Resnik et al, 2013;Williamson et al, 2016;Schwartz et al, 2014;Howes et al, 2014;Fraser et al, 2016;Tsugawa et al, 2015;Nguyen et al, 2014;De Choudhury et al, 2014;Tsugawa et al, 2015;Nadeem, 2016;Reece et al, 2017;Guntuku et al, 2017;. Depression is characterized by low mood, a lack of interest, cognitive and psychomotor impairment, and suicidal ideation.…”

“…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). The latter approach uses high-precision patterns such as "I was diagnosed with depression."…”