Uncovering Social Network Activity Using Joint User and Topic Interaction

TL;DR

This paper introduces MIC, a novel model using multidimensional Hawkes processes to jointly analyze user behavior and information cascade interactions in social networks, providing better modeling and visualization of information spread.

Contribution

The paper presents MIC, a new joint user and cascade interaction model based on marked multidimensional Hawkes processes, improving upon existing methods for social network activity analysis.

Findings

MIC outperforms existing models in synthetic and real data experiments.

MIC provides insightful visualizations of social network activity.

The model captures complex interplay between user activity and information cascades.

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.