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...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40339/1/Predicting%20mental%20health%20disorder%20on%20Twitter.pdf http://umpir.ump.edu.my/id/eprint/40339/2/Predicting%20mental%20health%20disorder%20on%20twitter%20using%20machine%20learning%20techniques_ABS.pdf http://umpir.ump.edu.my/id/eprint/40339/ https://doi.org/10.1109/ICSECS58457.2023.10256420 |
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| Summary: | 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. |
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