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Identifying 'Influencers' on Twitter

Identifying 'Influencers' on Twitter

This video was recorded at Fourth ACM International Conference on Web Search and Data Mining - WSDM 2011. Word-of-mouth diffusion of information is of great interest to planners, marketers and social network researchers alike. In this work we investigate the attributes and relative influence of 1.6M Twitter users by tracking 39 million diffusion events that took place on the Twitter follower graph over a two month interval in 2009. We find that the largest cascades tend to be generated by users who have been influential in the past and from URLs that were rated more interesting and/or elicited more positive feelings by workers on Mechanical Turk. However, individual-level predictions of which 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, and find that 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.


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