Modelling User Influence and Rumor Propagation on Twitter using Hawkes Processes

Understanding the spread of rumors on online social networks (OSNs) is crucial for designing strategies to detect and mitigate rumor propagation. Previous studies analysing rumor propagation have focused on summarising the static measurements of propagation-based information cascades. But static features are unable to capture the dynamic nature of information propagation across time. In this paper, we employed two generative models, Multivariate Hawkes process (MHP) and marked Hawkes process (marked HP) to model user influence and the dynamics of rumor propagation. Using the MHP model, we were able to derive a novel measurement of user influence in information propagation, namely, the influence rate. We then employed the marked HP model and considered various mark measurements including the proposed influence rate to provide new insights into differentiating between rumor and non-rumor propagation, and among different types of rumor propagation. Our analysis on Twitter rumor datasets clearly showed that users play different roles (i.e., possess different influence rates) across different categories of source tweets. Moreover, different categories of source tweets have different patterns of diffusion. In particular, rumor cascades typically attracted more influential users at the early stage of cascades, and they are more likely to generate more retweets than non-rumor cascades. Among different types of rumors, false rumors diffused faster than true rumors.