The intensive spread of information via social media platforms has increased the speed of transmission of both factual and speculative information, thus making it more challenging to understand the mechanisms of diffusion and the construction of the echo chambers. Previous studies mainly focus on either the network structure or textual information in isolation, which limits the scope of examination of the diffusion mechanisms. This paper presents an integrated analytical framework that simultaneously captures linguistic cues, user interaction patterns and predictive patterns based on artificial intelligence. The framework distinguishes between macro-level topic propagation and micro-level user-to-user diffusion and implements processes of determining the misinformation pathways and echo-chamber organization. The pipeline of the tripartite analysis is outlined, which includes text processing and linguistic feature extraction, social network and structural analysis building, and diffusion prediction with the help of the machine-learning tool. The suggested framework provides a methodological basis of studying diffusion behaviour within social media settings and can be used in misinformation surveillance, opinion-dynamics study, and social-policy oriented social-media evaluation. Combining linguistic, network, and AI approaches, the given work forms an extensive methodological foundation of future empirical diffusion research.