The asynchronous nature of communications on social network sites creates a unique opportunity for studying how posting content interacts with individuals' engagement. This study focuses on the behavioral changes occurring hours before and after contribution to better understand the changing needs and preferences of contributors. Using observational data analysis of individuals' activity on Facebook, we test hypotheses regarding the motivations for site visits, changes in the distribution of attention to content, and shifts in decisions to interact with others. We find that after posting content people are intrinsically motivated to visit the site more often, are more attentive to content from friends (but not others), and choose to interact more with friends (in large part due to reciprocity). In addition, contributors are more active on the site hours before posting and remain more active for less than a day afterwards. Our study identifies a unique pattern of engagement that accompanies contribution and can inform the design of social network sites to better support contributors.

On many social networking web sites such as Facebook and Twitter, resharing or reposting functionality allows users to share others' content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has focused on analyzing and characterizing such cascades, a recent, parallel line of work has argued that the future trajectory of a cascade may be inherently unpredictable. In this work, we develop a framework for addressing cascade prediction problems. On a large sample of photo reshare cascades on Facebook, we find strong performance in predicting whether a cascade will continue to grow in the future. We find that the relative growth of a cascade becomes more predictable as we observe more of its reshares, that temporal and structural features are key predictors of cascade size, and that initially, breadth, rather than depth in a cascade is a better indicator of larger cascades. This prediction performance is robust in the sense that multiple distinct classes of features all achieve similar performance. We also discover that temporal features are predictive of a cascade's eventual shape. Observing independent cascades of the same content, we find that while these cascades differ greatly in size, we are still able to predict which ends up the largest.