  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.
