In this paper we investigate the attributes and relative in\ufb02uence of 1.6M Twitter users by tracking 74 million di\ufb00usion events that took place on the Twitter follower graph over a two month interval in 2009. Unsurprisingly, we \ufb01nd that the largest cascades tend to be generated by users who have been in\ufb02uential in the past and who have a large number of followers. We also \ufb01nd 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 \ufb01nd that predictions of which particular user or URL will generate large cascades are relatively unreliable. We conclude, therefore, that word-of-mouth di\ufb00usion can only be harnessed reliably by targeting large numbers of potential in\ufb02uencers, thereby capturing average e\ufb00ects. Finally, we consider a family of hypothetical marketing strategies, de\ufb01ned by the relative cost of identifying versus compensating potential \u201cin\ufb02uencers.\u201d We \ufb01nd that although under some circumstances, the most in\ufb02uential users are also the most cost-e\ufb00ective, under a wide range of plausible assumptions the most cost-e\ufb00ective performance can be realized using \u201cordinary in\ufb02uencers\u201d\u2014 individuals who exert average or even less-than-average in\ufb02uence.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper we investigate the attributes and relative in\ufb02uence of 1.6M Twitter users by tracking 74 million di\ufb00usion events that took place on the Twitter follower graph over a two month interval in 2009. 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