Development of a Model for Screening Students’ Emotional Consultation Needs Based on Social Media Keywords in the Context of Depression Risk
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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% of the 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.
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