Psychological Stress Detection from Social Media Data using a Novel Hybrid Model - Ali, Mohammed Mahmood; Hajera, Shaikha (International Journal of Computer Sciences and Engineering, 2025)
Psychological stress is a biggest threat to human’s health. Hence, it is vital to detect and manage stress before it turns into severe problem. However, conventional stress detection strategies rely on psychological scales and physiological devices, which require active individual participation making it labor-consuming and expensive. With the rapid evolution of social media networks, people are willing to sharetheir everyday events and moods via social media platforms, making it practicable to leverage this online social media content for stress detection as these data timely reflect user’s real-life emotional state. To automatically predict stress, we have defined a set of stress-related textual ‘F = f1, f2, f3, f4’, visual ‘vF = vf1, vf2’, and social ‘sf’ features, and thenproposed a hybrid model Psychological Stress Detection (PSD) - a Probabilistic Naïve Bayes Classifier combined with Visual (Hue, Saturation, Value) and Social modules,to leverage text, image and social interaction information for stress detection from social media contentExperimental results show that the proposed PSD model improves the detection performance,when compared to TensiStrength and Teenchat frameworkPSD achieves 95% of Precision rate. PSD model would be useful in developing stress detection tools for mental health agencies and individuals