|
Development of a Model for Screening Students’ Emotional Consultation Needs Based on Social Media Keywords in the Context of Depression Risk |
|---|---|
| รหัสดีโอไอ | |
| Creator | Supunnee Sompong |
| Title | Development of a Model for Screening Students’ Emotional Consultation Needs Based on Social Media Keywords in the Context of Depression Risk |
| Contributor | Supot Seebut, Kanisa Chodjuntug, Kalyarat Boonyajahn |
| Publisher | Faculty of Science, Ubon Ratchathani University |
| Publication Year | 2569 |
| Journal Title | Journal of Science and Science Education |
| Journal Vol. | 9 |
| Journal No. | 1 |
| Page no. | 62-75 |
| Keyword | Depression Risk, Screening, Emotional Consultation, Social Media, Logistic Regression |
| URL Website | https://so04.tci-thaijo.org/index.php/JSSE/ |
| Website title | Journal of Science and Science Education |
| ISSN | ISSN 2697-410X |
| Abstract | This research aimed to analyze keywords in social media posts reflecting signs of depression and to develop a predictive model for emotional consultation needs among students. The study was conducted with a sample of 400 students from the Faculty of Science, Ubon Ratchathani University. The research instruments included a questionnaire on the usage of 10 specific keywords and an emotional consultation screening tool adapted from the Patient Health Questionnaire-9 (PHQ-9). The results revealed that 53.50% ofthe participants had a history of posting their feelings on social media, with "Sad" (55.25%) being the most frequently used keyword. Multiple Logistic Regression analysis identified six keywords with statistical significance in predicting the need for emotional consultation: "Bored," "Sad," "Nobody understands," "Tired," "Want to disappear for a while," and "Cannot take it anymore". Notably, the keyword "Bored" had the highest impact on the probability of needing consultation (Odds Ratio = 6.351). Furthermore, the developed model was implemented into a Python-based prototype program to serve as a preliminary screening tool for monitoring students' mental health in educational settings effectively. |