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 ördinary influencers"---individuals who exert average or even less-than-average influence.
%0 Conference Paper
%1 bakshy2011everyones
%A Bakshy, Eytan
%A Hofman, Jake M.
%A Mason, Winter A.
%A Watts, Duncan J.
%B Proceedings of the Fourth ACM International Conference on Web Search and Data Mining
%C New York, NY, USA
%D 2011
%I ACM
%K analysis diffusion influence network sna social toread twitter
%P 65--74
%R 10.1145/1935826.1935845
%T Everyone's an Influencer: Quantifying Influence on Twitter
%U http://doi.acm.org/10.1145/1935826.1935845
%X 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 ördinary influencers"---individuals who exert average or even less-than-average influence.
%@ 978-1-4503-0493-1
@inproceedings{bakshy2011everyones,
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.},
acmid = {1935845},
added-at = {2013-11-27T17:37:27.000+0100},
address = {New York, NY, USA},
author = {Bakshy, Eytan and Hofman, Jake M. and Mason, Winter A. and Watts, Duncan J.},
biburl = {https://www.bibsonomy.org/bibtex/235e5e1d54532914a9964ed4d6e966514/jaeschke},
booktitle = {Proceedings of the Fourth ACM International Conference on Web Search and Data Mining},
description = {Everyone's an influencer},
doi = {10.1145/1935826.1935845},
interhash = {60a83d55d35db8df957e39f9d4a16a05},
intrahash = {35e5e1d54532914a9964ed4d6e966514},
isbn = {978-1-4503-0493-1},
keywords = {analysis diffusion influence network sna social toread twitter},
location = {Hong Kong, China},
numpages = {10},
pages = {65--74},
publisher = {ACM},
series = {WSDM '11},
timestamp = {2014-07-28T15:57:31.000+0200},
title = {Everyone's an Influencer: Quantifying Influence on Twitter},
url = {http://doi.acm.org/10.1145/1935826.1935845},
year = 2011
}