Identifying PTSD symptoms using machine learning techniques on social media
Post-traumatic stress disorder (PTSD) is a mental health illness brought on by watching or experiencing a horrific incident. Flashbacks, nightmares, acute anxiety, and uncontrolled thoughts about the unforgettable incident are the possible symptoms faced by PTSD sufferers. The PTSD diagnosis is usua...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40325/ http://umpir.ump.edu.my/id/eprint/40325/1/Identifying%20PTSD%20symptoms%20using%20machine.pdf http://umpir.ump.edu.my/id/eprint/40325/2/Identifying%20PTSD%20symptoms%20using%20machine%20learning%20techniques%20on%20social%20media_ABS.pdf |
| _version_ | 1848826017888600064 |
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| author | Muhamad Aiman, Ibrahim Nur Hafieza, Ismail Nur Shazwani, Kamarudin Nur Syafiqah, Mohd Nafis Ahmad Fakhri, Ab Nasir |
| author_facet | Muhamad Aiman, Ibrahim Nur Hafieza, Ismail Nur Shazwani, Kamarudin Nur Syafiqah, Mohd Nafis Ahmad Fakhri, Ab Nasir |
| author_sort | Muhamad Aiman, Ibrahim |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Post-traumatic stress disorder (PTSD) is a mental health illness brought on by watching or experiencing a horrific incident. Flashbacks, nightmares, acute anxiety, and uncontrolled thoughts about the unforgettable incident are the possible symptoms faced by PTSD sufferers. The PTSD diagnosis is usually done by a mental health specialist based on the symptoms that the person has, and the task is very time-consuming. Due to the widespread use of social media in recent years, it has opened up the opportunity to explore PTSD signs in users' postings on Twitter. The content-sharing feature available on this platform has allowed its users to share personal experiences, thoughts, and feelings that could reflect their psychological status. Thus, the goal of this work is to identify the PTSD symptom from text posting on Twitter. The crawled text posting is filtered and trained on selected machine learning and deep learning methods. The experiment results show that the support vector machine performed the best with 91% accuracy compared to others. This extracted model could be used in identifying PTSD symptoms on social media. |
| first_indexed | 2025-11-15T03:38:08Z |
| format | Conference or Workshop Item |
| id | ump-40325 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:38:08Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-403252024-04-16T04:07:10Z http://umpir.ump.edu.my/id/eprint/40325/ Identifying PTSD symptoms using machine learning techniques on social media Muhamad Aiman, Ibrahim Nur Hafieza, Ismail Nur Shazwani, Kamarudin Nur Syafiqah, Mohd Nafis Ahmad Fakhri, Ab Nasir QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Post-traumatic stress disorder (PTSD) is a mental health illness brought on by watching or experiencing a horrific incident. Flashbacks, nightmares, acute anxiety, and uncontrolled thoughts about the unforgettable incident are the possible symptoms faced by PTSD sufferers. The PTSD diagnosis is usually done by a mental health specialist based on the symptoms that the person has, and the task is very time-consuming. Due to the widespread use of social media in recent years, it has opened up the opportunity to explore PTSD signs in users' postings on Twitter. The content-sharing feature available on this platform has allowed its users to share personal experiences, thoughts, and feelings that could reflect their psychological status. Thus, the goal of this work is to identify the PTSD symptom from text posting on Twitter. The crawled text posting is filtered and trained on selected machine learning and deep learning methods. The experiment results show that the support vector machine performed the best with 91% accuracy compared to others. This extracted model could be used in identifying PTSD symptoms on social media. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40325/1/Identifying%20PTSD%20symptoms%20using%20machine.pdf pdf en http://umpir.ump.edu.my/id/eprint/40325/2/Identifying%20PTSD%20symptoms%20using%20machine%20learning%20techniques%20on%20social%20media_ABS.pdf Muhamad Aiman, Ibrahim and Nur Hafieza, Ismail and Nur Shazwani, Kamarudin and Nur Syafiqah, Mohd Nafis and Ahmad Fakhri, Ab Nasir (2023) Identifying PTSD symptoms using machine learning techniques on social media. In: 8th International Conference on Software Engineering and Computer Systems, ICSECS 2023 , 25-27 August 2023 , Penang. pp. 392-395. (192961). ISBN 979-835031093-1 (Published) https://doi.org/10.1109/ICSECS58457.2023.10256290 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Muhamad Aiman, Ibrahim Nur Hafieza, Ismail Nur Shazwani, Kamarudin Nur Syafiqah, Mohd Nafis Ahmad Fakhri, Ab Nasir Identifying PTSD symptoms using machine learning techniques on social media |
| title | Identifying PTSD symptoms using machine learning techniques on social media |
| title_full | Identifying PTSD symptoms using machine learning techniques on social media |
| title_fullStr | Identifying PTSD symptoms using machine learning techniques on social media |
| title_full_unstemmed | Identifying PTSD symptoms using machine learning techniques on social media |
| title_short | Identifying PTSD symptoms using machine learning techniques on social media |
| title_sort | identifying ptsd symptoms using machine learning techniques on social media |
| 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/40325/ http://umpir.ump.edu.my/id/eprint/40325/ http://umpir.ump.edu.my/id/eprint/40325/1/Identifying%20PTSD%20symptoms%20using%20machine.pdf http://umpir.ump.edu.my/id/eprint/40325/2/Identifying%20PTSD%20symptoms%20using%20machine%20learning%20techniques%20on%20social%20media_ABS.pdf |