Mental health forums are online communities where people express their issues and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self-harm. Moderators need to respond to these severe posts in a timely manner to prevent potential selfharm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We present a framework for triaging user content into four severity categories which are defined based on indications of self-harm ideation. Our models are based on a feature-rich classification framework which includes lexical, psycholinguistic, contextual and topic modeling features. Our approaches improve the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F-1 scores). Using the proposed model, we analyze the mental state of users and we show that overall, long-term users of the forum demonstrate a decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need.
Online mental health forums provide users with an anonymous support platform that is facilitated by moderators responsible for finding and addressing critical posts, especially those related to self-harm. Given the seriousness of these posts, it is important that the moderators are able to locate these critical posts quickly in order to respond with timely support. We approached the task of automatically triaging forum posts as a multiclass classification problem. Our model uses a supervised classifier with various features including lexical, psycholinguistic, and topic modeling features. On a dataset of mental forum posts from ReachOut.com 1 , our approach identified critical cases with a F-score of over 80%, showing the effectiveness of the model. Among 16 participating teams and 60 total runs, our best run achieved macro-average F1-score of 41% for the critical categories (The best score among all the runs was 42%).
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