Maximizing the spread of influence through a social network
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Tracking Information Epidemics in Blogspace
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A data-based approach to social influence maximization
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Large-scale behavioral targeting with a social twist
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Information diffusion in social networks: observing and affecting what society cares about
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Object matching in tweets with spatial models
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From chatter to headlines: harnessing the real-time web for personalized news recommendation
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Exploring social influence via posterior effect of word-of-mouth recommendations
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The role of social networks in information diffusion
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Towards Twitter context summarization with user influence models
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Comparing the use of social networking and traditional media channels for promoting citizen science
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Which targets to contact first to maximize influence over social network
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Identifying influential twitter users in the 2011 egyptian revolution
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Personalized influence maximization on social networks
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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.