Abstract
Studying the mechanism of retweeting is useful for understanding the information diffusion in Online Social Networks (OSNs). In this paper, we examine a number of topological features that may affect the retweeting behavior. We apply the Follow Model to formulate the user relations and then propose Relationship Commitment Adjacency Matrix (RCAM) to present the connectivity between users in OSNs. We define three meta-paths to identify the people who may retweet. With these meta-paths, various instance-paths are revealed in the retweeting prediction problem. A framework based on Conditional Random Field model is developed and implemented with the data from Sina Weibo. The case study obtains the results of retweeting prediction with the indices of precision larger than 61% and recall larger than 58%.
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References
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.: Measuring user influence in twitter: The million follower fallacy. In: Proceedings of 4th International AAAI Conference on Weblogs and Social Media (ICWSM), pp. 10–17 (2010)
Kwak, H., Lee, C., Park, H.: What is Twitter, a Social Network or a News Media? In: The 19th International World Wide Web Conference, Raleigh, USA, pp. 591–600. ACM (2010)
Sun, Y., Han, J., Aggarwal, C.C., Chawla, N.V.: When Will It Happen?: Relationship Prediction in Heterogeneous Information Networks. In: Proceedings of Int. Conf. on Web Search and Data Mining, WSDM 2012, pp. 663–672 (2012)
De Sandes, E.F.O., Weigang, L., de Melo, A.C.M.A.: Logical Model of Relationship for Online Social Networks and Performance Optimizing of Queries. In: Wang, X.S., Cruz, I., Delis, A., Huang, G. (eds.) WISE 2012. LNCS, vol. 7651, pp. 726–736. Springer, Heidelberg (2012)
Lu, X., Yu, Z., Guo, B., Zhou, X.: Predicting the content dissemination trends by repost behavior modeling in mobile social networks. Journal of Network and Computer Applications 42, 197–207 (2014)
Weigang, L., Sandes, E.F., Zheng, J., de Melo, A.C., Uden, L.: Querying dynamic communities in online social networks. Journal of Zhejiang University SCIENCE C 15(2), 81–90 (2014)
Weigang, L., Jianya, Z., Liu, Y.: Retweeting Prediction Using Relationship Commited Adjacency Matrix. In: BraSNAM – II Brazilian Workshop on Social Network Analysis and Mining, Maceio. Proceedings of CSBC 2013, pp. 1561–1572 (2013)
Peng, H.K., Zhu, J., Piao, D., Yan, R., Zhang, Y.: Retweet modeling using conditional random fields. In: IEEE 11th International Conference on Data Mining Workshops (ICDMW), pp. 336–343 (2011)
Lafferty, J., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning (ICML), pp. 282–289 (2001)
Hong, L., Dan, O., Davison, B.: Predicting popular messages in twitter. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 57–58 (2011)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.: Measuring user influence in twitter: The million follower fallacy. In: Proceedings of 4th International AAAI Conference on Weblogs and Social Media (ICWSM), pp. 10–17 (2010)
Hastie, T., Mazumder, R., Lee, J., Zadeh, R.: Matrix completion and low-rank SVD via fast alternating least squares. Journal of Machine Learning Research (to appear, 2015).
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Weigang, L., Zheng, J. (2015). Retweeting Prediction Using Meta-Paths Aggregated with Follow Model in Online Social Networks. In: Omatu, S., et al. Distributed Computing and Artificial Intelligence, 12th International Conference. Advances in Intelligent Systems and Computing, vol 373. Springer, Cham. https://doi.org/10.1007/978-3-319-19638-1_2
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DOI: https://doi.org/10.1007/978-3-319-19638-1_2
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