Statistics of co-occurring keywords in confined text messages on Twitter
- 136 Downloads
Online social media such as the micro-blogging site Twitter has become a rich source of real-time data on online human behaviors. Here we analyze the occurrence and co-occurrence frequency of keywords in user posts on Twitter. From the occurrence rate of major international brand names, we provide examples of predictions of brand-user behaviors. From the co-occurrence rates, we further analyze the user-perceived relationships between international brand names and construct the corresponding relationship networks. In general the user activity on Twitter is highly intermittent and we show that the occurrence rate of brand names forms a highly correlated time signal.
KeywordsEuropean Physical Journal Special Topic Probability Distribution Function Occurrence Rate Twitter User Sentiment Score
Unable to display preview. Download preview PDF.
- 5.J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance, M. Hurst, Proc. SIAM Int. Conf. on Data Mining (SDM) (2007)Google Scholar
- 11.J. Kulshrestha, F. Kooti, A. Nikravesh, P.K. Gummadi, Geographic Dissection of the Twitter Network. In ICWSM (2012)Google Scholar
- 12.A. Mislove, S. Lehmann, Y.Y. Ahn, J.P. Onnela, J.N. Rosenquist, Understanding the Demographics of Twitter Users. In ICWSM (2011)Google Scholar
- 16.S. Asur, B.A. Huberman (2010, August), Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on Vol. 1, IEEE, p. 492Google Scholar
- 17.H. Kwak, C. Lee, H. Park, S. Moon, What is Twitter, a social network or a news media? Proceedings of the 19th international conference on World Wide Web (2010), p 591Google Scholar
- 19.J. Mathiesen, P. Yde, M.H. Jensen, Scient. Rep. 2 (2012)Google Scholar
- 20.M. Steyvers, J.B. Tenenbaum, Cognit. Sci. 29, 4178 (2005)Google Scholar