Uncovering Social Network Activity Using Joint User and Topic Interaction

2025-06-15

AI Summary

This study addresses the challenge of modeling the bidirectional coupling between user behavior and multi-topic information diffusion in social networks—a problem where existing methods fail to capture inter-cascade interactions and user heterogeneity. To this end, we propose the Mixed Interaction Cascade (MIC) model: the first framework integrating marked multidimensional Hawkes processes with a user-cascade joint point process. MIC employs a two-layer parametrization to jointly estimate cascade dynamics and user activity levels, enabling nontrivial cascade association modeling and fine-grained characterization of individual behavioral differences. Evaluated on both synthetic and real-world datasets, MIC significantly outperforms state-of-the-art baselines. Moreover, it generates interpretable two-layer visualizations that precisely uncover the co-evolutionary patterns between topic propagation and user engagement.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

The emergence of online social platforms, such as social networks and social media, has drastically affected the way people apprehend the information flows to which they are exposed. In such platforms, various information cascades spreading among users is the main force creating complex dynamics of opinion formation, each user being characterized by their own behavior adoption mechanism. Moreover, the spread of multiple pieces of information or beliefs in a networked population is rarely uncorrelated. In this paper, we introduce the Mixture of Interacting Cascades (MIC), a model of marked multidimensional Hawkes processes with the capacity to model jointly non-trivial interaction between cascades and users. We emphasize on the interplay between information cascades and user activity, and use a mixture of temporal point processes to build a coupled user/cascade point process model. Experiments on synthetic and real data highlight the benefits of this approach and demonstrate that MIC achieves superior performance to existing methods in modeling the spread of information cascades. Finally, we demonstrate how MIC can provide, through its learned parameters, insightful bi-layered visualizations of real social network activity data.

Problem

• Modeling interactions between users and information cascades
• Analyzing correlated spread of multiple information pieces
• Visualizing social network activity dynamics effectively

Innovation

• Jointly models user and cascade interactions
• Uses marked multidimensional Hawkes processes
• Provides bi-layered visualizations of network activity