Recognizing Temporal Linguistic Expression Pattern of Individual with Suicide Risk on Social Media

TL;DR

This study analyzes temporal linguistic patterns on social media to predict individual suicide risk, demonstrating that certain linguistic features can effectively inform risk classification models with over 60% accuracy.

Contribution

It introduces a novel approach using time series analysis of social media language to assess suicide risk, highlighting specific linguistic features that influence prediction performance.

Findings

Temporal linguistic features impact model accuracy

Prediction accuracy exceeds 60%

Social media data is effective for suicide risk detection

Abstract

Suicide is a global public health problem. Early detection of individual suicide risk plays a key role in suicide prevention. In this paper, we propose to look into individual suicide risk through time series analysis of personal linguistic expression on social media (Weibo). We examined temporal patterns of the linguistic expression of individuals on Chinese social media (Weibo). Then, we used such temporal patterns as predictor variables to build classification models for estimating levels of individual suicide risk. Characteristics of time sequence curves to linguistic features including parentheses, auxiliary verbs, personal pronouns and body words are reported to affect performance of suicide most, and the predicting model has a accuracy higher than 0.60, shown by the results. This paper confirms the efficiency of the social media data in detecting individual suicide risk. Results of this study may be insightful for improving the performance of suicide prevention programs.