Identification of Influential Nodes and Discourse Features within Synthetical Hierarchical Communities in Online Social Networks

Abstract

Social fragmentation and information gap are leading to a growing number of communication barriers and social issues. To analyse root causes of the above phenomena from the view of interactive influence, we develop two models to identify influential nodes and discourse features in online social networks to reveal their influence on information dissemination in synthetical hierarchical communities. Firstly, a Node Influence Calculation Model is constructed based on network topology by integrating multi-dimensional indicators such as degree centrality, closeness centrality, and betweenness centrality, to evaluate the influence of nodes with the holistic information in online social network. Secondly, a Discourse Features Model is built based on semantic information by incorporating topics and sentimental features with fine-grained semantic analysis to decode the strategic adjustments and sentimental polarization mechanisms of influential nodes. Finally, with empirical research in real networks, the above two models effectively calculate the influence of nodes and reveal their roles in reinforcing communication effects, cross-community connections, and fostering community integration through themes and emotions. The findings can provide theoretical basis and guiding strategies to promote balanced information dissemination for online social network management, public opinion guidance and societal cohesion