Multi-topic information diffusion in social networks

Information can span multiple topics, and individuals differ in how their attention is distributed across those topics. In online social networks, individuals are more likely to connect with others who attend to similar topics, making such similarity-based social ties important channels for information diffusion. Yet much diffusion research represents information as a single undifferentiated item rather than distinguishing among the topics it contains, thereby overlooking how the relevance of social ties to diffusion varies across those topics and leaving unclear how diffusion outcomes change when information spans multiple topics. To address this gap, we develop a topic-level extension of the Linear Threshold Model that defines influence weights and adoption thresholds separately for each topic and uses an aggregation operator to combine topic-specific adoption conditions into an overall adoption decision. Simulation analysis shows that multi-topic information shapes diffusion outcomes not simply by expanding the potential-adopter pool, but by changing its composition: diffusion depends on how potential adopters’ attention is distributed across the topics contained in the information. The results further show that less favorable network conditions for diffusion can be partly offset when the potential-adopter pool includes a larger share of individuals whose attention spans multiple information topics. This study clarifies how multi-topic information spreads in online social networks and identifies the informational conditions under which it can diffuse across larger and more heterogeneous populations.