An Influence Strength Measurement via Time-Aware Probabilistic Generative Model for Microblogs
Micro-blogging such as Twitter provides users a platform to participate in the discussion of topics and find more interesting friends. While this large number can generate notable diversity and not all influence strengths between users are the same, it also makes measure the influence strength more accurately, that not only rated as binary friendship relations, challenging and interesting. In this work, we develop a time-aware probabilistic generative model to estimate the influence strength by taking the time interval, relationship of following, and the post content into consideration. In particular, the Gibbs sampling is employed to perform approximate inference, and the interval of time and the multi-path influence propagation is incorporated to estimate the indirect influence strength more microscopically according to the propagation of words. Comprehensive experiments has been conducted on a real data set from Twitter, which contains about 0.26 million users and 2.7 million tweets, to evaluate the performance of our proposed approach. As indicated, the experimental results validate the effectiveness of our approach. Furthermore, we also observe that the influence strength ranking by our model is less correlative with the method which ranks the influence strength according to the number of common friends.
Keywordsinfluence strength probabilistic generative model microblogs social media social network
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- 1.Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: Proc. of the 19th International World Wide Web Conference, WWW 2010, Raleigh, USA, pp. 591–600 (April 2010)Google Scholar
- 2.Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining topic-level influence in heterogeneous networks. In: Proc. of the 19th ACM Conference on Information and Knowledge Management, CIKM 2010, Toronto, Ontario, Canada, pp. 199–208 (October 2010)Google Scholar
- 4.Guo, Z., Zhang, Z., Zhu, S., Chi, Y., Gong, Y.: Knowledge discovery from citation networks. In: Proc. of the 9th IEEE International Conference on Data Mining, ICDM 2009, Miami, Florida, USA, pp. 800–805 (December 2009)Google Scholar
- 5.Dietz, L., Bickel, S., Scheffer, T.: Unsupervised prediction of citation influences. In: Proc. of the 24th International Conference on Machine Learning, ICML 2007, Corvallis, Oregon, USA, pp. 233–240 (June 2007)Google Scholar
- 6.Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: Proc. of the 19th International World Wide Web Conference, WWW 2010, Raleigh, USA, pp. 981–990 (April 2010)Google Scholar
- 7.Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: Proc. of the 27th International Conference on Human Factors in Computing Systems, CHI 2009, Boston, USA, pp. 211–220 (April 2009)Google Scholar
- 8.Kahanda, I., Neville, J.: Using transactional information to predict link strength in online social networks. In: Proc. of the 3rd International AAAI Conference on Weblogs and Social Media, ICWSM 2009, San Jose, California, USA, pp. 74–81 (May 2009)Google Scholar