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...
| 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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| Online Access: | http://umpir.ump.edu.my/id/eprint/40339/ 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 |
| _version_ | 1848826021475778560 |
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| author | Lim, Shi Ru Nur Shazwani, Kamarudin Nur Hafieza, Ismail Nik Ahmad, Hisham Ismail Nor Ashikin, Mohamad Kamal |
| author_facet | Lim, Shi Ru Nur Shazwani, Kamarudin Nur Hafieza, Ismail Nik Ahmad, Hisham Ismail Nor Ashikin, Mohamad Kamal |
| author_sort | Lim, Shi Ru |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:38:12Z |
| format | Conference or Workshop Item |
| id | ump-40339 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:38:12Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-403392024-04-16T04:12:20Z http://umpir.ump.edu.my/id/eprint/40339/ Predicting mental health disorder on twitter using machine learning techniques Lim, Shi Ru Nur Shazwani, Kamarudin Nur Hafieza, Ismail Nik Ahmad, Hisham Ismail Nor Ashikin, Mohamad Kamal QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) 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. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40339/1/Predicting%20mental%20health%20disorder%20on%20Twitter.pdf pdf en http://umpir.ump.edu.my/id/eprint/40339/2/Predicting%20mental%20health%20disorder%20on%20twitter%20using%20machine%20learning%20techniques_ABS.pdf Lim, Shi Ru and Nur Shazwani, Kamarudin and Nur Hafieza, Ismail and Nik Ahmad, Hisham Ismail and Nor Ashikin, Mohamad Kamal (2023) Predicting mental health disorder on twitter using machine learning techniques. In: 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023 , 25-27 August 2023 , Penang. pp. 23-27. (192961). ISBN 979-835031093-1 (Published) https://doi.org/10.1109/ICSECS58457.2023.10256420 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Lim, Shi Ru Nur Shazwani, Kamarudin Nur Hafieza, Ismail Nik Ahmad, Hisham Ismail Nor Ashikin, Mohamad Kamal Predicting mental health disorder on twitter using machine learning techniques |
| title | Predicting mental health disorder on twitter using machine learning techniques |
| title_full | Predicting mental health disorder on twitter using machine learning techniques |
| title_fullStr | Predicting mental health disorder on twitter using machine learning techniques |
| title_full_unstemmed | Predicting mental health disorder on twitter using machine learning techniques |
| title_short | Predicting mental health disorder on twitter using machine learning techniques |
| title_sort | predicting mental health disorder on twitter using machine learning techniques |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/40339/ http://umpir.ump.edu.my/id/eprint/40339/ 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 |