An Emotion Aware Dual-Context Model for Suicide Risk Assessment on Social Media

Suicide risk assessment on social media is an essential task for mental health surveillance. Although extensively studied, existing works share the following limitations, including (1) insufficient exploitation of Non-SuicideWatch posts, and (2) ineffective consideration of the fine-grained emotional information in both SuicideWatch and Non-Suicide-Watch posts. To tackle these issues, we propose an emotion aware dual-context model to predict suicide risk. Specifically, SuicideWatch posts that contain psychological crisis are leveraged to obtain the suicidal ideation context. Then, given that suicidal ideation is not instantaneous and Non-SuicideWatch posts can provide essential information, we encode the emotion-related features and emotional changes with variable time intervals, revealing users’ mental states. Finally, the embeddings of SuicideWatch, Non-SuicideWatch, LIWC feature, and posting time are concatenated and poured into a fully-connected network for suicide risk level recognition. Extensive experiments are conducted to validate the effectiveness of our method. In results, our scheme outperforms the first place in CLPsych2019 task B by 4.9% on Macro-F1 and achieves a 10.7% increase on F1 of Severe Risk label than the first place in CLPsych2019 task A that only uses SuicideWatch posts.