Author(s)

Lijing Sun ¹, Yu Luo ¹

Affiliation(s)

¹ College of Psychology, Guizhou Normal University, Huaxi, Guiyang, Guizhou, 550025, China

Corresponding Author

Yu Luo

ABSTRACT

Recent years, with the development of big data, artificial intelligence, natural language processing and other technologies, the research on automatic mental health assessment driven by social network data has provided great convenience for the detection of depression and the suicidal tendency identification. In this study, machine learning and natural language processing technology had been adopted to identify depression of 203 Sina Weibo users. The recognition accuracy of 88.2% is achieved by using Gradient Boosting algorithm. Further, the suicidal tendency of 1204 Sina Weibo texts was identified and the Gradient Boosting algorithm was used to achieve an accuracy of 82.4% of Sina Weibo users with depression tendency. Then the suicidal ideation was analyzed by Beck Scale for Suicide Ideation (BSS) of Sina Weibo users with severe depression tendency. The results showed that most Sina Weibo users with severe depression tendency would hide their suicidal intention. It is also found that the word frequency related to suicidal ideation in the text of Sina Weibo users with severe depression tendency has no correlation with suicide intensity and suicide risk through statistical correlation analysis. The score of Beck Depression Inventory (BDI) of Sina Weibog users with severe depression tendency has a certain positive correlation with the score of suicidal ideation and suicide risk. The above identification and analysis of depression and suicidal tendency of Sina Weibog users will help quickly tap the depression and suicidal mood of users, so as to assist psychological workers and medical staff to carry out early warning intervention and avoid tragedy. 

KEYWORDS

Sina Weibo users, Text, Depression, Suicidal ideation, Machine learning

CITE THIS PAPER

Lijing Sun, Yu Luo, Identification and analysis of depression and suicidal tendency of Sina Weibo users based on machine learning. Advances in Educational Technology and Psychology (2022) Vol. 6: 108-117. DOI: http://dx.doi.org/10.23977/aetp.2022.060916.

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