Modeling Topical Information Diffusion over Microblog Networks

TLDR: This work designs a predictive topical spreading activation model (TopSPA) that utilizes the social connection structures of users, along with their topic affinities, to model the information flow and empirically validate the model on multiple social event datasets on Twitter.

Abstract: Traditional information spread and activation models on social networks, fail to take user interests towards specific content (topics) into account. To this, we propose a predictive topical spreading activation model (TopSPA). Following cues from the well-known spreading activation (SPA) model, we design the TopSPA algorithm to include the affinity of users to given topics. TopSPA utilizes the social connection structures of users, along with their topic affinities, to model the information flow. We use topic-based skew in energy seeding and energy propagation resistance in the network to form our overall information diffusion model. We empirically validate our model on multiple social event datasets on Twitter, predicting information diffusion over the social graph with a high accuracy.