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

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