SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions
Table 1: Comparison between the number of self-reported diagnosed users per condition in the dataset of coppersmith2015adhd and ours (smhd).
['[BOLD] Condition', 'Twitter, (Copper- smith et al, 2015)', 'Reddit, smhd (ours)']
Our work has the following significant distinctions compared to existing social media datasets related to mental health. This makes the Twitter language use rather different from real life discussions. Instead, we use data from Reddit, an interactive discussion-centric forum without any length constraints. Our dataset contains up to two orders of magnitude more diagnosed individuals for each condition than the Twitter dataset by \newcitecoppersmith2015adhd, making it suitable for exploring more recent data-driven learning methods (see We choose our control users in a systematic way that makes classification experiments on the dataset realistic. We normalize language usage between the users: by removing specific mental health signals and discussions, we focus on patterns of language in normal (general) discussions. While our dataset creation method is close to \newciteyates2017depression, we extend theirs by investigating multiple high-precision matching patterns to identify self-reported diagnoses for a range of conditions. Part of our patterns are obtained through synonym discovery. Considering relevant synonyms from reliable sources increases the variety of the diagnosed users and linguistic nuances. We also explore nine common mental health conditions while \newciteyates2017depression focus only on depression. We explore classification methods for identifying mental health conditions through social media language and provide detailed analysis that helps us understand the differences in language usage between conditions, and between diagnosed users and controls. Our contributions are as follows: (i) We investigate the creation of high-precision matching patterns to identify self-reported diagnoses of nine different mental health conditions. (ii) We introduce a large-scale dataset of nine mental health conditions that has significant extensions to existing datasets and we make our data publicly available. Our dataset includes users who might suffer from more than one condition, thus allowing language study of interacting mental conditions. (iii) We investigate language characteristics of each mental health group. (iv) We explore classification methods for detecting users with various mental health conditions.