Computing Geographical Scopes of Web Resources
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
The structure of information pathways in a social communication network
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Find me if you can: improving geographical prediction with social and spatial proximity
Proceedings of the 19th international conference on World wide web
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
You are where you tweet: a content-based approach to geo-locating twitter users
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Modeling Information Diffusion in Implicit Networks
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Enhanced sentiment learning using Twitter hashtags and smileys
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Proceedings of the 20th international conference on World wide web
Smoothing techniques for adaptive online language models: topic tracking in tweet streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Analyzing the dynamic evolution of hashtags on Twitter: a language-based approach
LSM '11 Proceedings of the Workshop on Languages in Social Media
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Object matching in tweets with spatial models
Proceedings of the fifth ACM international conference on Web search and data mining
What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities
Proceedings of the fifth ACM international conference on Web search and data mining
YouTube around the world: geographic popularity of videos
Proceedings of the 21st international conference on World Wide Web
Spatial influence vs. community influence: modeling the global spread of social media
Proceedings of the 21st ACM international conference on Information and knowledge management
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In this paper, we tackle the problem of predicting what online memes will be popular in what locations. Specifically, we develop data-driven approaches building on the global footprint of 755 million geo-tagged hashtags spread via Twitter. Our proposed methods model the geo-spatial propagation of online information spread to identify which hashtags will become popular in specific locations. Concretely, we develop a novel reinforcement learning approach that incrementally updates the best geo-spatial model. In experiments, we find that the proposed method outperforms alternative linear regression based methods.