Background Suicide is a global public health and mental health problem. With the rapid development of internet technology, more and more people tend to express their suicidal tendencies and suicidal intentions online. The difference of emotional characteristics between high and low risk of suicide messages should be analyzed to help identify suicide risk and provide early intervention. Methods The "tree hole"intelligent robot captures message data, then randomly selects the same number of high and low suicide risk messages manually, and the high frequency keywords of high and low suicide risk messages are obtained by word segmentation and a TF-IDF algorithm. The keywords are analyzed by Gephi software, and the emotion dictionary provided by Boson is used to judge the emotional tendency of high and low suicide risk users. Results The emotional score of high suicide risk messages was -3.511 ∼ 2.514, averaging (-0.225±0.405), while the total score of low suicide risk messages was -4.547 ∼ 3.403, averaging (-0.121±0.628). Low suicide risk messages mainly focused on negative emotions, interpersonal relationships and social support, while high suicide risk messages mainly centered on invited suicide, means, locations and time of suicide. Conclusion There are differences in emotional characteristics between high and low suicide risk messages. The higher the suicide risk, the more obvious the negative tendency of the users' emotions. More attention is needed to the greater potential for suicide among this group of users and psychological support and interventions should be included.
|Title of host publication||ISAIMS 2021|
|Subtitle of host publication||Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences|
|Publisher||Association for Computing Machinery|
|Number of pages||6|
|Publication status||Published - Oct 2021|
|Event||2nd International Symposium on Artificial Intelligence for Medicine Sciences, ISAIMS 2021 - Virtual, Online, China|
Duration: 29 Oct 2021 → 31 Oct 2021
|Name||ACM International Conference Proceeding Series|
|Conference||2nd International Symposium on Artificial Intelligence for Medicine Sciences, ISAIMS 2021|
|Period||29/10/21 → 31/10/21|
Bibliographical noteFunding Information:
Sincere thanks are given to Sharon R. Redding (EdD, RN, CNE) for assistance in editing. Funding: This study is supported by the National Natural Science Foundation of China (No. 72174152), Wuhan university provincial college students' innovation and entrepreneurship training program (S2020305001, S202110486194, S202110486196) and Wuhan university 2019 school of medicine students' innovation and entrepreneurship training program (MS2019046).
© 2021 ACM.
- Artificial intelligence
- Emotional characteristics analysis
- Suicide risk
- Weibo "tree hole"