Everyone's an influencer

TLDR

The study investigates the attributes and influence of 1.6 million Twitter users and evaluates marketing strategies for targeting influencers. The authors analyzed 74 million diffusion events on the Twitter follower graph over two months in 2009 and modeled the relative cost of identifying versus compensating potential influencers. They found that large cascades are driven by previously influential users with many followers and that URLs rated interesting or positive spread more, yet predicting specific users or URLs is unreliable, so word‑of‑mouth diffusion is best harnessed by targeting many potential influencers, with ordinary influencers often being the most cost‑effective.

Abstract

In this paper we investigate the attributes and relative influence of 1.6M Twitter users by tracking 74 million diffusion events that took place on the Twitter follower graph over a two month interval in 2009. Unsurprisingly, we find that the largest cascades tend to be generated by users who have been influential in the past and who have a large number of followers. We also find that URLs that were rated more interesting and/or elicited more positive feelings by workers on Mechanical Turk were more likely to spread. In spite of these intuitive results, however, we find that predictions of which particular user or URL will generate large cascades are relatively unreliable. We conclude, therefore, that word-of-mouth diffusion can only be harnessed reliably by targeting large numbers of potential influencers, thereby capturing average effects. Finally, we consider a family of hypothetical marketing strategies, defined by the relative cost of identifying versus compensating potential "influencers." We find that although under some circumstances, the most influential users are also the most cost-effective, under a wide range of plausible assumptions the most cost-effective performance can be realized using "ordinary influencers"---individuals who exert average or even less-than-average influence.