Suicide Risk Prediction by Tracking Self-Harm Aspects in Tweets: NUS - IDS at the CLP sych 2021 Shared Task (2021)

Abstract: We describe our system for identifying users at-risk for suicide based on their tweets developed for the CLPsych 2021 Shared Task. Based on research in mental health studies linking self-harm tendencies with suicide, in our system, we attempt to characterize self-harm aspects expressed in user tweets over a period of time. To this end, we design SHTM, a Self-Harm Topic Model that combines Latent Dirichlet Allocation with a self-harm dictionary for modeling daily tweets of users. Next, differences in moods and topics over time are captured as features to train a deep learning model for suicide prediction.

## introduction social media portals provide outlets for people to express their thoughts and emotions, and researchers have noted that user writings on social media contain signs and symptoms of various mental disorders @xcite . due to this reason, automated methods for identifying individuals "at risk" for various conditions such as depression, suicide, and addiction based on their online activity is an upcoming, recent research topic @xcite . in this paper, we focus on suicide, a leading cause of mortality among younger population @xcite and address the problem of identifying individuals at-risk for suicide as part of the clpsych 2021 shared task. in particular, we make use of the well-established link between self-harm tendencies and suicide @xcite and study the expression of self-harm moods in user tweets. our contributions are as follows: ## conclusions and future work we presented sht m , our topic model for representing self-harm aspects expressed in social media texts. we used features based on self-harm mood changes and topic changes in tweets over time within a deep learning model to predict suicidal users. to the best of our knowledge, we are the first to employ topic models for studying mood characterization in context of suicide risk. several topic models were proposed in previous works for incorporating label information and improving prediction tasks @xcite @xcite . in future, we aim to incorporate emotion lexicons @xcite ) into these models and suitably extend them to characterize temporal mood trends @xcite of users with mental health issues such as depression, ptsd, and suicide @xcite .