Predicting Mental Health Disorder on Twitter Using Machine Learning Techniques

• 出版年: 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023
• 第一著者: 2-s2.0-85175457067
• フォーマット: Conference paper
• 言語: English
• 出版事項: Institute of Electrical and Electronics Engineers Inc. 2023
• オンライン･アクセス: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175457067&doi=10.1109%2fICSECS58457.2023.10256420&partnerID=40&md5=8979be900d6fde87e679105d0307a604

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.