Advanced Temporal Modelling of Mental Health Trends using Online user Narratives and Smart Recommendation Systems

The increasing amount of digital communication is giving us insight into people's emotional states, allowing mental health signals to emerge through day-to-day online behaviours. Their baseline signalling is observed over time as patterns through these digital traces and emits meaningful signals that can relate to early detection of mental health problems and facilitating people to reach out for help in timely manner [1]. This research conducts advanced temporal analysis of mental health expressions captured from user-generated content, using sequential modelling and intelligent recommender systems to make sense of and interpret changing behaviour cues. The research indicates contemporary analysis approaches, such as deep learning neural sequence models, attention-based transformer encoders, and hybrid recommendation systems have greater utility in identifying emotional change and deviation from normative wellbeing [4], [10]. The findings indicate that temporal behaviour change analysis, in conjunction with adaptive recommendation strategies, can provide users with higher fidelity alerts, timed contextual guidance, and tailored interventions of people experiencing distress [3], [14]. Given what has been learned from past studies and emerging analytic technologies, this work has established a framework for system-building and proposed a foundation for future mental-health technological applications that will provide continuous assessment and real-time estimation of behaviour and support engagement in real-world online contexts [8], [20].