Suicide is a serious social problem and public health problem, it is urgent for us to study and carry out practical work. Some scholars have begun to collect information about users' psychological health through social network platform, including the recognition of suicide risk, but there is still no real suicide risk identification and intervention system to put into practice. Through this study, we hope to explore a new way to use Weibo to conduct suicidal risk identification and intervention, find more individuals who are in suicide risks, and actively help them to reduce their suicide risk.
This topic mainly includes three studies:
The first study constructs structural equation of influence factors of suicidal ideation for the Weibo users and compares the difference of upper-and-lower groups of suicidal ideation in using characteristics of Weibo language. This study will provide theoretical support for suicide recognition and intervention work.
The second study is divided into two experiments based on risk identification of Weibo text: Experiment 1, the accuracy of two kinds text reorganization ways in identification of single Weibo text is compared, namely keywords selection and model selection through machine learning. Experiment 2, to investigate the effect of suicide risk identification based on micro-blog text.
The third study explores suicide risk intervention based on private messages of Weibo. Firstly, to explore the feasibility of intervention by focus-group interviews. Secondly, to send private messages to help the individuals who are in suicide risk, and estimate the effect of sending private messages.
Main results are as follows:
(1) In terms of using characteristics of Weibo language, there are apparent differences（P<0.05）in the use frequency of upper and lower groups in the word- "anxiety" and word of "death". By fitting of data and presuppose model 2, the fitting is good and the structural equation model is obtained by further path optimization.
(2) Compared with hit rate of keyword filtering-0.47, false rate- 0.66, effect that adopts the computer training model is better, hit rate is 0.58, the false rate is 0.25. And individuals identified by micro-blog's text of suicide risk are indeed high risk groups for suicide.
(3) Through interviews of focus group, private messages can be used as a means of preliminary intervention to contact users to provide help. By sending private messages, we get positive feedback from the users. The results show that, although for the majority of users, we cannot directly change the status quo. But there are still some users called the hotline for help because of the private message. And for self-help intervention system, the majority of users expressed a more positive attitude.
The above researches show that the modern booming social media platforms, represented by Weibo, have provided new opportunity for suicide identification and intervention work. Through the analysis of the Weibo data, the regional restriction can be broken through for screening of high-risk groups, and risky individual can be voluntarily contacted and get help through the private messages, thus obtaining certain effect. Through early warning and intervention mechanism of Weibo, the scope and efficiency of suicide prevention work can be effectively improved.