Detecting Shifts in Public Discourse from Offline to Online Using Deep Learning

Increasingly, discussions that once took place in social environments are transitioning to digital platforms. The role of news media is significant in shaping and enhancing discussions around many topics. This study argues that health-related topics in public discourse, transitioning from offline to online, necessitate rigorous validation. That is why this study proposed the application of deep learning techniques to the boundaries and deviation of accuracies in health-related topics by analyzing health-related tweets from major news outlets such as BBC, CNN, CBC, and Reuters. The study developed LSTM and CNN classifiers to categorize content pertinent to the discourse following the formal deep learning process and employed a sequence of VAEs to verify the learnability and stability of the classifiers. The LSTM demonstrated superior performance compared to CNN, attaining validation accuracies of 98.4% on BBC and CNN, 97.8% on CBC, and 97.3% on Reuters. The optimal configuration of our LSTM achieved a precision of 98.69%, a recall of 98.20%, and an F1-score of 97.90% and recorded the lowest false positive rate, at 1.30%. This provided us with the optimal overall equilibrium for operational oversight. The VAE runs demonstrated that the model exhibited stability and the ability to generalize across different sources, achieving approximately 99.6% for Reuters and around 98.4% for BBC. The findings confirm that deep learning models are capable of reliably tracking the online migration of health discourse driven by news media. This provides a solid foundation for near-real-time monitoring of public engagement and for informing sustainable healthcare recommendation systems.