Cross-community opinion clustering via opinion-aware Louvain and Friedkin–Johnsen modeling

The increasing spread of misinformation and ideological polarization in online social networks promotes the formation of isolated and radicalized communities. Understanding how opinions evolve within and across these communities is essential for tracking disinformation, identifying influential users, and analyzing social dynamics. This paper proposes a novel framework for detecting cross-community groups of users who share similar opinions over time. The approach combines a temporal opinion graph based on the Friedkin–Johnsen (FJ) model with a refined Louvain clustering algorithm. The graph representation captures opinion dynamics by integrating user stubbornness, influence relationships, and temporal interactions across connected communities. Building on this model, the proposed clustering method enhances modularity optimization with an opinion-alignment refinement step, allowing clusters to be adjusted according to node-to-cluster opinion similarity. Experiments on real-world Reddit data show that the proposed method improves intra-cluster opinion homogeneity, increases inter-cluster opinion separation, and more effectively identifies cross-community groups of like-minded users compared to standard Louvain clustering. These results suggest that incorporating opinion alignment into community detection better preserves opinion structures in social networks while maintaining competitive modularity.