- Research Article
- 10.1109/tcss.2026.3664984
Uncovering Social Network Activity Using Joint User and Topic Interaction
- Jan 01, 2026
- IEEE Transactions on Computational Social Systems
- Guy Abel + 3 more +3
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 article, we introduce the mixture of interacting cascades (<monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MIC</monospace>), 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 <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MIC</monospace> achieves superior performance to existing methods in modeling the spread of information cascades. Finally, we demonstrate how <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MIC</monospace> can provide, through its learned parameters, insightful bi-layered visualizations of real social network activity data.
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