Follower count is important to Twitter users: it can indicate popularity and prestige. Yet, holistically, little is understood about what factors – like social behavior, message content, and network structure – lead to more followers. Such in-formation could help technologists design and build tools that help users grow their audiences. In this paper, we study 507 Twitter users and a half-million of their tweets over 15 months. Marrying a longitudinal approach with a negative binomial auto-regression model, we find that variables for message content, social behavior, and network structure should be given equal consideration when predicting link formations on Twitter. To our knowledge, this is the first longitudinal study of follow predictors, and the first to show that the relative contributions of social behavior and mes-sage content are just as impactful as factors related to social network structure for predicting growth of online social networks. We conclude with practical and theoretical impli-cations for designing social media technologies.
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