Maximizing influence in a competitive social network: a follower's perspective
Proceedings of the ninth international conference on Electronic commerce
Proceedings of the first workshop on Online social networks
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Outtweeting the twitterers - predicting information cascades in microblogs
WOSN'10 Proceedings of the 3rd conference on Online social networks
A study of rumor control strategies on social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Characterization of the twitter @replies network: are user ties social or topical?
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Correcting for missing data in information cascades
Proceedings of the fourth ACM international conference on Web search and data mining
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
A framework for quantitative analysis of cascades on networks
Proceedings of the fourth ACM international conference on Web search and data mining
Limiting the spread of misinformation in social networks
Proceedings of the 20th international conference on World wide web
Proceedings of the 20th international conference on World wide web
On word-of-mouth based discovery of the web
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Dynamical classes of collective attention in twitter
Proceedings of the 21st international conference on World Wide Web
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We present the first comprehensive characterization of the diffusion of ideas on Twitter, studying more than 5.96 million topics that include both popular and less popular topics. On a data set containing approximately 10 million users and a comprehensive scraping of 196 million tweets, we perform a rigorous temporal and spatial analysis, investigating the time-evolving properties of the subgraphs formed by the users discussing each topic. We focus on two different notions of the spatial: the network topology formed by follower-following links on Twitter, and the geospatial location of the users. We investigate the effect of initiators on the popularity of topics and find that users with a high number of followers have a strong impact on topic popularity. We deduce that topics become popular when disjoint clusters of users discussing them begin to merge and form one giant component that grows to cover a significant fraction of the network. Our geospatial analysis shows that highly popular topics are those that cross regional boundaries aggressively.