For Whom to Tweet? Evidence from a Large-Scale Social Media Platform

Abstract

We study the effects of peer-group sizes on content generating and sharing in a large-scale and influential social media platform. User-generated content, particularly tweets in social media, disseminates information and exerts social influence. However, 50% of the users in this platform post less than 6 tweets per month and contribute to less than 15% of the total tweets in stock, while the top 10% post on average 40 tweets a month and contribute to more than half of the tweets in stock. We attribute the highly unbalanced contribution to a user's conflicting incentives of free-riding and maximizing social influence. We exploit the asymmetry of a user's peer groups (followers and followees, groups of people following and being followed by the user respectively) to disentangle these incentives, and devise empirical strategies to deal with the endogenous formation of one's networks. We find asymmetric effects, both in signs and sizes, of followers and followees on content generating and sharing. A larger group of followers leads a user to tweet more, while a larger group of followees leads a user to tweet less. As the follower effects dominate the followee effects in size, our simulations indicate that the platform could increase the number of total tweets by 25% if it randomly adds 1% new links to existing links. Targeting occasional tweeters is even more effective in promoting the activeness of this social media platform.