Unveiling the Potential of AI-Driven Social Media: Applications of Intelligent Data Analysis in News Communication

News dissemination in the context of digitalization is a research focus in the field of multimedia and social networks. Stable and accurate modeling and analysis can effectively promote the development of journalism. The theoretical basis of this study is that social networks, complex networks and multimedia networks. Through deep modeling, this study constructs a new two-layer news dissemination model. In terms of topology, the high-order network centrality optimization is used to obtain the dynamic characteristics of user relationships and dissemination paths. In the part of the social network fusion multimedia analysis, this study designed an adaptive random walk algorithm to address the data sparsity and uncertainty of trust relationships. This algorithm can further optimize the trust calculation indicators of social networks. Furthermore, in the optimization of the multimedia network modeling, this study improved the stability of resource allocation through matching exchange operations to ensure the accessibility of news. In the experimental part, this study uses the topological analysis method of complex networks to visualize the structural characteristics and information dissemination efficiency of news dissemination network. This study provides a new theoretical basis for the networking and intelligence of news dissemination, and expands the scope of traditional research to a new paradigm of computer algorithm integration.