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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: Lim, Shi Ru, Nur Shazwani, Kamarudin, Nur Hafieza, Ismail, Nik Ahmad, Hisham Ismail, Nor Ashikin, Mohamad Kamal
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
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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spelling my.ump.umpir.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 https://doi.org/10.1109/ICSECS58457.2023.10256420
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
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
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.
format Conference or Workshop Item
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
title Predicting mental health disorder on twitter using machine learning techniques
title_short 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_sort predicting mental health disorder on twitter using machine learning techniques
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
url 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
_version_ 1822924223376523264
score 13.235362

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