Spatial variation in search engine queries
Proceedings of the 17th international conference on World Wide Web
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th 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
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
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
Traveling trends: social butterflies or frequent fliers?
Proceedings of the first ACM conference on Online social networks
Hi-index | 0.00 |
We conduct a study of the spatio-temporal dynamics of Twitter hashtags through a sample of 2 billion geo-tagged tweets. In our analysis, we (i) examine the impact of location, time, and distance on the adoption of hashtags, which is important for understanding meme diffusion and information propagation; (ii) examine the spatial propagation of hashtags through their focus, entropy, and spread; and (iii) present two methods that leverage the spatio-temporal propagation of hashtags to characterize locations. Based on this study, we find that although hashtags are a global phenomenon, the physical distance between locations is a strong constraint on the adoption of hashtags, both in terms of the hashtags shared between locations and in the timing of when these hashtags are adopted. We find both spatial and temporal locality as most hashtags spread over small geographical areas but at high speeds. We also find that hashtags are mostly a local phenomenon with long-tailed life spans. These (and other) findings have important implications for a variety of systems and applications, including targeted advertising, location-based services, social media search, and content delivery networks.