Predicting Mental Health Disorder on Twitter Using Machine Learning Techniques

Social media gives young people a place to voice their difficulties and trade opinions on current events in the digital era. Therefore, it is possible to analyze human behavior using internet media. However, the illness of mental disorder is common yet often ignored. Social media makes it possible t...

全面介紹

書目詳細資料
發表在:8th International Conference on Software Engineering and Computer Systems, ICSECS 2023
主要作者: 2-s2.0-85175457067
格式: Conference paper
語言:English
出版: Institute of Electrical and Electronics Engineers Inc. 2023
在線閱讀:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175457067&doi=10.1109%2fICSECS58457.2023.10256420&partnerID=40&md5=8979be900d6fde87e679105d0307a604
實物特徵
總結:Social media gives young people a place to voice their difficulties and trade opinions on current events in the digital era. Therefore, it is possible to analyze human behavior using internet media. However, the illness of mental disorder is common yet often ignored. Social media makes it possible to identify mental health disorders in large populations. Many efforts have been made to evaluate individual postings using machine learning techniques to identify people with mental health conditions on social media. This study attempted to predict mental health disorders among Twitter users using machine learning techniques. Support Vector Machine (SVM), Decision Tree, and Naive Bayes are three examples of machine learning approaches applied in this study. To assess the algorithms, the performance and accuracy of these three algorithms are compared. © 2023 IEEE.
ISSN:
DOI:10.1109/ICSECS58457.2023.10256420