Retweets|but Not Just Retweets: Quantifying and Predicting Inuence on Twitter

Abstract

There has recently been a sharp uptick in interest among researchers and private firms in determining how to quantify influence on the microblogging site Twitter. We restrict our attention solely to celebrities, and using data collected from Twitter APIs in February and March 2012, we explore four different influence metrics for a group of 60 prominent and well-followed individuals. We find that retweet-based influence is the most significant type of influence, but other effects—like the adoption of hashtags and links—are comparable in terms of generated impressions, and are governed by fundamentally different dynamics. We use the insights from our analysis to develop predictive models of retweets, hashtag and link adoptions, and increases in follower counts. We find that, across different types of influence, the degree to which a celebrity is discussed on Twitter is an extremely useful predictor, while follower counts are comparatively less predictive.