David Garcia, ETH Zurich
Pavlin Mavrodiev, Daniele Casati, Frank Schweitzer
We present a study of popularity, reputation, and social influence through large-scale digital traces across 7 years in the Twitter social network. We process network information on more than 40 million users, calculating new global measures of reputation that build on the D-core decomposition and the bow-tie structure of the Twitter follower network. We integrate our measurements of popularity, reputation, and social influence in a study to evaluate what keeps users active, what makes them more popular, and what determines their influence. We find that there is a range of values in which the risk of a user becoming inactive grows with popularity and with reputation. Popularity in Twitter resembles a proportional growth process that is faster in its strongly connected component, and that can be accelerated by reputation when users are already popular. Analyzing activity, we find that popular users have a larger extent of social influence while reputable users can influence other users that are on average more influential themselves. The explanatory and predictive power of our methods shows that global network metrics are better predictors of inactivity and social influence, calling for analyses that go beyond local metrics like the amount of followers.