Social media platforms play a crucial role in regulating public discourse. Recognizing the importance of understanding this complex phenomenon a large body of research has been published in attempts to model how information spreads within these platforms. These models are termed information propagation models. The majority of the existing information propagation models attempt to capture the causal relationship between to two information spreading events through modeling the probabilities of information transmission between the two users or through capturing the temporal correlations that exist between the events.
While these models have been successful in the past, they fail to capture the various properties that have emerged in the recent past. One emerging property that has been presented in the recent analysis is the role the content of information plays in regulating the patterns of information spread. Specifically, social scientists believe that in the presence of large amounts of information, users tend to interact with items that help confirm their own views.
This thesis explores a possible method to incorporate user-specific and event-specific features to existing information propagation models by scaling the edge parameters. Through modeling the scaling factors to capture the phenomena of selective exposure due to confirmation bias, we showcase the ability of our approach to capturing complex social dynamics. Through experiments on both synthetic and real-world datasets, we validate the advantages that could be gained over the existing models. The presented approach exhibits clearly visible performance gains on the network recovery task and performed competitively against the baselines
