publication . Other literature type . Conference object . 2011

quantifying influence on twitter

Eytan Bakshy; Duncan J. Watts; Winter Mason; Jake M. Hofman;
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  • Published: 01 Jan 2011
  • Publisher: Association for Computing Machinery (ACM)
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 ...
Subjects
free text keywords: Relative cost, Word-of-mouth marketing, Computer science, Influencer marketing, Spite, World Wide Web, Advertising, Graph, Feeling, media_common.quotation_subject, media_common
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