A Data-driven Study of Influences in Twitter Communities

TL;DR

This study analyzes Twitter user influence through large-scale data, revealing non-power-law influence distribution, community reciprocity, homophily, hierarchy, and the impact of initial retweeters, introducing a new influence diffusion model.

Contribution

It introduces the first influencer (FI) diffusion model tailored for Twitter, outperforming existing models in stability and accuracy for influence prediction.

Findings

Influence distribution is non-power-law.

Strong reciprocity exists among users.

Retweet behavior is heavily influenced by initial posters.

Abstract

This paper presents a quantitative study of Twitter, one of the most popular micro-blogging services, from the perspective of user influence. We crawl several datasets from the most active communities on Twitter and obtain 20.5 million user profiles, along with 420.2 million directed relations and 105 million tweets among the users. User influence scores are obtained from influence measurement services, Klout and PeerIndex. Our analysis reveals interesting findings, including non-power-law influence distribution, strong reciprocity among users in a community, the existence of homophily and hierarchical relationships in social influences. Most importantly, we observe that whether a user retweets a message is strongly influenced by the first of his followees who posted that message. To capture such an effect, we propose the first influencer (FI) information diffusion model and show through extensive evaluation that compared to the widely adopted independent cascade model, the FI model is more stable and more accurate in predicting influence spreads in Twitter communities.