| 其他摘要 | From the perspective of today's mobile social trends, more and more people choose to share life on the network platform and express their feelings. Social media is not only a tool for modern people to express themselves, communicate and participate in group activities, but also a window to reflect their psychological state, and an important channel for implementing online suicide prevention. Sina Weibo is the most representative social media in China. The user's posting data on Sina Weibo contains language features related to suicidal behavior. Extracting the language features presented by these user groups when using Weibo can establish a suicide risk prediction model, automatically identify users with suicidal thoughts, and further carry out online intervention for these potential suicide groups.Based on the existing research results and Sina Weibo's online active suicide prevention process, this topic proposed an optimized idea for group identification of suicidal ideation, and conducted a study on the establishment and performance of suicide recognition models. The results show that: The suicidal idea recognition model based on multi-feature weighting method (MFWF method), its precision, recall, F-measure and accuracy reached 0.89, 0.88, 0.88, 0.89, which are higher than data-driven method (p<0.01). It is an improvement to the data-driven method of single feature extraction, and its recognition effect is significantly better than the existing data-driven methods.In the study of group intervention of suicidal ideation, word frequency analysis was performed on the content of the user's replies. The results show that 10 words including "thank you", "doctor", "emotion" and "depression" are high-frequency words, and the high frequency use of mental disease-related nouns indicates that users who participate in online suicide interventions appear significantly pay attention to depression and related clinical features, or they were plagued by persistent depression and pessimism, which leads to specific questions, anxieties and discussions. Thus, the recognition model could detect the potential high-risk suicide users with depression and depressive symptoms.In this paper, the content of each user's reply message is recorded according to certain classification standards. After the classification of 3626 suicidal users, 3284 people can be identified. In general, 90.6% of the users' replies confirmed that they had different degrees of suicidal ideation when they published information, which shows that the accuracy of the recognition model is good. At the same time, this study also builds a classification basis for further understanding of different users in the suicidal ideation group.In the intervention research of high self-exposure (HSD) users, statistics show that psychological factors, social factors and intimacy factors are the three main suicide ideation sources, specifically to the segmentation factors, which are mental illness, social pressure and parent-child problems. These three factors appear in a dual way, which should be paid close attention in suicide intervention. In the statistics of mental diseases for HSD users, the number of users with depression and schizophrenia accounts for 65.9% of the total group, and the number of users with depression was the largest, with a case ratio of 84.5%. This further illustrates that most users with high self-exposure are indeed at high risk of suicide, and they have obvious group characteristics in the source of suicide ideation and the distribution of mental diseases.The results of this study not only provide us a better method for screening the groups with suicidal ideation, but also facilitate targeted support with the help of in-depth understanding about group characteristics, and help to establish a more systematic and reasonable online work flow of proactive suicide prevention from identification to intervention. |
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