ABSTRACT
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.
- Universal transverse mercator coordinate system, November 2012.Google Scholar
- L. Backstrom, J. Kleinberg, R. Kumar, and J. Novak. Spatial variation in search engine queries. In Proceeding of the 17th international conference on World Wide Web, pages 357--366. ACM, 2008. Google ScholarDigital Library
- L. Backstrom, E. Sun, and C. Marlow. Find me if you can: improving geographical prediction with social and spatial proximity. In Proceedings of the 19th international conference on World wide web, pages 61--70. ACM, 2010. Google ScholarDigital Library
- C. Bauckhage. Insights into internet memes. Proc. ICWSM2011, pages 42--49, 2011.Google Scholar
- A. Brodersen, S. Scellato, and M. Wattenhofer. Youtube around the world: geographic popularity of videos. In Proceedings of the 21st international conference on World Wide Web, pages 241--250. ACM, 2012. Google ScholarDigital Library
- Z. Cheng, J. Caverlee, and K. Lee. You are where you tweet: a content-based approach to geo-locating twitter users. In Proceedings of the 19th ACM international conference on Information and knowledge management, pages 759--768. ACM, 2010. Google ScholarDigital Library
- Z. Cheng, J. Caverlee, K. Lee, and D. Sui. Exploring millions of footprints in location sharing services. AAAI ICWSM, 2011.Google Scholar
- E. Cunha, G. Magno, G. Comarela, V. Almeida, M. Gonçalves, and F. Benevenuto. Analyzing the dynamic evolution of hashtags on twitter: a language-based approach. In Proceedings of the Workshop on Language in Social Media (LSM 2011), pages 58--65, 2011. Google ScholarDigital Library
- N. Dalvi, R. Kumar, and B. Pang. Object matching in tweets with spatial models. In Proceedings of the fifth ACM international conference on Web search and data mining, pages 43--52. ACM, 2012. Google ScholarDigital Library
- D. Davidov, O. Tsur, and A. Rappoport. Enhanced sentiment learning using twitter hashtags and smileys. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pages 241--249. Association for Computational Linguistics, 2010. Google ScholarDigital Library
- J. Ding, L. Gravano, and N. Shivakumar. Computing geographical scopes of web resources. ., 2000.Google Scholar
- Foursquare. About foursquare, April 2013.Google Scholar
- B. A. Huberman, D. M. Romero, and F. Wu. Social Networks that Matter: Twitter Under the Microscope. Social Science Research Network Working Paper Series, Dec. 2008.Google Scholar
- K. Y. Kamath, J. Caverlee, Z. Cheng, and D. Z. Sui. Spatial influence vs. community influence: modeling the global spread of social media. In Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM '12, pages 962--971, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, pages 591--600. ACM, 2010. Google ScholarDigital Library
- K. Lerman and R. Ghosh. Information contagion: An empirical study of the spread of news on digg and twitter social networks. In Proceedings of 4th International Conference on Weblogs and Social Media (ICWSM), 2010.Google Scholar
- J. Leskovec, L. Backstrom, and J. Kleinberg. Meme-tracking and the dynamics of the news cycle. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 497--506. ACM, 2009. Google ScholarDigital Library
- J. Lin, R. Snow, and W. Morgan. Smoothing techniques for adaptive online language models: topic tracking in tweet streams. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 422--429. ACM, 2011. Google ScholarDigital Library
- A. Noulas, S. Scellato, C. Mascolo, and M. Pontil. An empirical study of geographic user activity patterns in foursquare. ICWSM'11, 2011.Google Scholar
- D. Romero, B. Meeder, and J. Kleinberg. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In Proceedings of the 20th international conference on World wide web, pages 695--704. ACM, 2011. Google ScholarDigital Library
- S. Scellato, A. Noulas, R. Lambiotte, and C. Mascolo. Socio-spatial properties of online location-based social networks. Proceedings of ICWSM, 11:329--336, 2011.Google Scholar
- O. Tsur and A. Rappoport. What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities. In Proceedings of the fifth ACM international conference on Web search and data mining, pages 643--652. ACM, 2012. Google ScholarDigital Library
- J. Yang and J. Leskovec. Modeling information diffusion in implicit networks. In Data Mining (ICDM), 2010 IEEE 10th International Conference on, pages 599--608. IEEE, 2010. Google ScholarDigital Library
Index Terms
- Spatio-temporal dynamics of online memes: a study of geo-tagged tweets
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