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Typically, online social influence is analyzed using a single metric approach. However, social influence is not monolithic; different users exercise different influences in different ways, and influence is correlated with the user and content-specific attributes. One such attribute could be whether the action is an initiation of a new post, a contribution to a post, or a sharing of an existing post. Thus, this dissertation uses this platform-independent action classification and models the influence as multiple entities and examines social networks through the perspective of behavioral influence propagation. Two empirical studies are present in this dissertation. The first study presents a novel method for tracking these influence relationships over time, which we call influence cascades, and presents a visualization technique to understand these cascades better. These influence patterns are investigated within and across online social media platforms using empirical data and comparing to a scale-free network as a null model. Our results show that characteristics of influence cascades and patterns of influence are, in fact, affected by the platform and the community of the users. The second study applies the same framework to re-construct interconnected social networks and explores the significance of cross-platform influence on social media users in the influence process. In particular, we explore the social dynamics of users with a higher number of social influence relationships across platforms, which we call interface users, and those with fewer social influence relationships across platforms, which we call core users. Our results find that interface users are more vulnerable to being influenced and influential than core users. Further, our results show that the interface users who are influenced to do initiation action exert significantly more influence on others than those who are influenced to contribute. |
