Characteristics of High Suicide Risk Messages From Users of a Social Network-Sina Weibo "Tree Hole".

Bing Xiang Yang,Zhisheng Huang,Guanghui Fu,Qian Liu,Dan Luo,Li Wen,Xiao Qin Wang,Wentian Li,Pan Chen,Fang Yang,Xin Yi Li,Junyong Zhu

doi:10.3389/fpsyt.2022.789504

Abstract

BackgroundPeople with suicidal ideation post suicide-related information on social media, and some may choose collective suicide. Sina Weibo is one of the most popular social media platforms in China, and “Zoufan” is one of the largest depression “Tree Holes.” To collect suicide warning information and prevent suicide behaviors, researchers conducted real-time network monitoring of messages in the “Zoufan” tree hole via artificial intelligence robots.ObjectiveTo explore characteristics of time, content and suicidal behaviors by analyzing high suicide risk comments in the “Zoufan” tree hole.MethodsKnowledge graph technology was used to screen high suicide risk comments in the “Zoufan” tree hole. Users' level of activity was analyzed by calculating the number of messages per hour. Words in messages were segmented by a Jieba tool. Keywords and a keywords co-occurrence matrix were extracted using a TF-IDF algorithm. Gephi software was used to conduct keywords co-occurrence network analysis.ResultsAmong 5,766 high suicide risk comments, 73.27% were level 7 (suicide method was determined but not the suicide date). Females and users from economically developed cities are more likely to express suicide ideation on social media. High suicide risk users were more active during nighttime, and they expressed strong negative emotions and willingness to end their life. Jumping off buildings, wrist slashing, burning charcoal, hanging and sleeping pills were the most frequently mentioned suicide methods. About 17.55% of comments included suicide invitations. Negative cognition and emotions are the most common suicide reason.ConclusionUsers sending high risk suicide messages on social media expressed strong suicidal ideation. Females and users from economically developed cities were more likely to leave high suicide risk comments on social media. Nighttime was the most active period for users. Characteristics of high suicide risk messages help to improve the automatic suicide monitoring system. More advanced technologies are needed to perform critical analysis to obtain accurate characteristics of the users and messages on social media. It is necessary to improve the 24-h crisis warning and intervention system for social media and create a good online social environment.

Highlights

Suicide is an important social issue and has become the second leading cause of death among 15–29 years old globally (1)
Posting suicide or self-harm information on social media is regarded as a signal of suicidal ideation, and it may increase the contagious effect of suicidality since suicidal behaviors may be learned from others (3–5)
The findings provide evidence for developing targeted long-term support programs on social media for high suicide risk users

Summary

Introduction

Suicide is an important social issue and has become the second leading cause of death among 15–29 years old globally (1). Identification of people with suicide risk is crucial for suicide prevention. These people usually do not actively seek help, so traditional methods such as self-reported ratings and structured interviews are ineffective in identifying suicide risk in time. Posting suicide or self-harm information on social media is regarded as a signal of suicidal ideation, and it may increase the contagious effect of suicidality since suicidal behaviors may be learned from others (3–5). People with suicidal ideation post suicide-related information on social media, and some may choose collective suicide. To collect suicide warning information and prevent suicide behaviors, researchers conducted real-time network monitoring of messages in the “Zoufan” tree hole via artificial intelligence robots Sina Weibo is one of the most popular social media platforms in China, and “Zoufan” is one of the largest depression “Tree Holes.” To collect suicide warning information and prevent suicide behaviors, researchers conducted real-time network monitoring of messages in the “Zoufan” tree hole via artificial intelligence robots

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Results

Discussion

Conclusion