Direct and Indirect Influence on Likes in Social Media

The present study investigates direct and indirect social contagion mechanisms in an online social network environment. Using a large-scale dataset comprising approximately 290,000 users from the VKontakte platform, we examine the factors associated with the probability that a user likes a post. Our analysis shows that, while demographic and structural characteristics of individual nodes, such as gender and degree, contribute to the observed dynamics, the strongest associations arise from activity in the user's local network. In particular, active nodes (users who have already liked the post) at distances d = 1 and d = 2 play a central role in shaping liking behavior. We find a substantial association between second-order activity and liking probability, which persists even in the absence of active direct neighbors and is consistent with indirect influence pathways in the network. No significant association is detected for nodes at distance three or beyond. The results also support the structural diversity hypothesis: the number of connected components among active friends is a significant predictor of liking.