Social Network Data-Driven Analysis of Behaviour: Social Media, Misinformation and Opinion Dynamics

Abstract In the digital age, social networks transformed how humans interact, share information and their opinions. However these platforms also facilitate the rapid misinformation that affecting public behaviour and their trust. This paper provides a data-driven overview of social media behavior, mechanism of misinformation spread, and models describe opinion dynamics. We examine network effects, cognitive factors, emotional factors for understanding and mitigating misinformation. Drawing on the large scale platforms, we apply computational models, sentiments and behavior analysis, and network diffusion techniques to measure how misinformation propagates and how opinion states evolve within interconnected networks. This comprehensive approach underscores the complexity of behavioural responses to misinformation and lays the groundwork for improved detection, mitigation, and intervention strategies that combine behavioural insights with data-driven modelling. The findings suggest that combating misinformation requires not only algorithmic solutions but also deeper understanding of human behaviour and opinion evolution in digitally connected societies.