Milad Eftekhar
Yashar Ganjali
ABSTRACT
Data available through social media and content sharing platforms present opportunities for analysis and mining. In the context of social networks, it is interesting to formalize and locate bursts of activities amongst users, related to a particular event and to report sets of socially connected users participating in such bursts. Such collections present new opportunities for understanding social events, and render new ways of online marketing.
In this paper, we model social information using two conceptualized graph models. The first one (the action graph) provides a detailed model of all activities of all users while the second one (the holistic graph) provides an aggregate view on each user in the social media. We also propose two models to define the notion of "burst". The first model (intrinsic burst model) takes the intrinsic characteristics of each user into account to recognize the bursty behaviors; while the second model (social burst model) considers neighbors' influences when identifying bursts. We provide two linear algorithms to detect bursts based on the proposed models. These algorithms have been extensively evaluated on a month of full Twitter dataset certifying the practicality of our approach. A detailed qualitative study of our techniques is also presented.
- G. De Francisci Morales, A. Gionis, and C. Lucchese. From chatter to headlines: harnessing the real-time web for personalized news recommendation. WSDM, pages 153--162, 2012. Google Scholar
Digital Library
- P. Domingos and M. Richardson. Mining the network value of customers. SIGKDD, pages 57--66, 2001. Google ScholarDigital Library
- J. Edmonds and R. M. Karp. Theoretical improvements in algorithmic effciency for network flow problems. J. ACM, 19:248--264, 1972. Google ScholarDigital Library
- J. Ford, L.R. and D. Fulkerson. Maximal flow through a network. Canadian Journal of Mathematics, 8:399--404, 1956.Google ScholarCross Ref
- M. R. Garey and D. S. Johnson. Computers and Intractability; A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York, NY, USA, 1990.Google ScholarDigital Library
- A. V. Goldberg and R. E. Tarjan. A new approach to the maximum-flow problem. J. ACM, 35(4):921--940, 1988. Google ScholarDigital Library
- D. M. Greig, B. T. Porteous, and A. H. Seheult. Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society. Series B, 51:271--279, 1989.Google ScholarCross Ref
- D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence in a social network. SIGKDD, pages 137--146, 2003. Google ScholarDigital Library
- J. Kleinberg. Bursty and hierarchical structure in streams. SIGKDD, pages 91--101, 2002. Google ScholarDigital Library
- A. Kotov, C. Zhai, and R. Sproat. Mining named entities with temporally correlated bursts from multilingual web news streams. WSDM, pages 237--246, 2011. Google ScholarDigital Library
- C. X. Lin, B. Zhao, Q. Mei, and J. Han. Pet: a statistical model for popular events tracking in social communities. SIGKDD, pages 929--938, 2010. Google ScholarDigital Library
- M. Mathioudakis, N. Bansal, and N. Koudas. Identifying, attributing and describing spatial bursts. VLDB Endowment, 3(1-2):1091--1102, Sept. 2010. Google ScholarDigital Library
- A. Pal and S. Counts. Identifying topical authorities in microblogs. WSDM, pages 45--54, 2011. Google ScholarDigital Library
- E. J. Ruiz, V. Hristidis, C. Castillo, A. Gionis, and A. Jaimes. Correlating financial time series with micro-blogging activity. WSDM, pages 513--522, 2012. Google ScholarDigital Library
- J. Weng, E.-P. Lim, J. Jiang, and Q. He. Twitterrank: finding topic-sensitive in uential twitterers. WSDM, pages 261--270, 2010. Google ScholarDigital Library
- Y. Zhu and D. Shasha. Effcient elastic burst detection in data streams. SIGKDD, pages 336--345, 2003. Google ScholarDigital Library
Index Terms
- Bursty subgraphs in social networks
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