Abstract
Tweets spread on social micro-blog bears some similarity to epidemic spread. Based on the findings from a user study on tweets’ short-term retweeting characteristics, we extend the classic Susceptible-Infected-Susceptible (SIS) epidemic model for tweet’s retweeting trend prediction, featured by the multiple retweeting peaks, retweeting lifetime, and total retweeting amount. We cluster micro-blog users with similar retweeting influence together, and train the model using the least square method on the historic retweeting datato obtain different groups’ retweeting rates. We demonstrate its effectiveness on a real micro-blog platform.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Boyd, D., Golder, S., Lotan, G.: Tweet, tweet, retweet: conversational aspects of retweeting on twitter. In: Proceedings of HICSS (2010)
Budak, C., Agrawal, D., Abbadi, A.: Structural trend analysis for online social networks. In: Proceedings of VLDB (2011)
Hethcote, H.: A thousand and one epidemic models. In: Levin, S.A. (ed.) Frontiers in Mathematical Biology. Lecture notes in Biomathematics, vol. 100, pp. 504–515. Springer, Heidelberg (1984)
Kim, H., Wang, K., Yoneki, E.: Finding influential neighbors to maximize informationdiffusion in twitter. In: Proceedings of IW3C2 (2014)
Kong, S., Feng, L., Sun, G., Luo, K.: Predicting lifespans of popular tweets in microblog. In: Proceedings of SIGIR (2012)
Lappas, T., Terzi, E.: Finding effectors in social networks. In: Proceedings of KDD (2010)
Lin, S., Kong, X., Yu, P.: Predicting trends in social networks via dynamic activeness model. In: Proceedings of CIKM (2013)
Lu, R., Yang, Q.: Trend analysis of news topics on twitter. In: Proceedings of Machine Learning and Computing (2012)
Petrović, S., Osborne, M., Lavrenko, V.: RT to win! predicting message propagation in twitter. In: Proceedings of AAAI (2010)
Prakash, B.A., Chakrabarti, D., Faloutsos, M., Valler, N., Faloutsos, C.: Threshold conditions for arbitrary cascade models on arbitrary networks. In: Proceedings of ICDM (2011)
Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In: Proceedings of SocialCom (2010)
Wang, X., McCallum, A.: Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of KDD (2006)
Yang, J., Counts, S.: Predicting the speed, scale, and range of information diffusion in twitter. In: Proceedings of AAAI (2010)
Yang, Z., Guo, J., Tang, J., Li, J., Zhang, L., Su, Z.: Understanding retweeting behaviors in social networks. In: Proceedings of CIKM (2010)
Zaman, T., Herbrich, R., van Gael, J., Stern, D.: Predicting information spreading in twitter. In: Proceedings of NIPS Workshop (2010)
Acknowledgement
The work is supported by National Natural Science Foundation of China (61373022, 61073004), and Chinese Major State Basic Research Development 973 Program (2011CB302203-2).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Feng, Z., Li, Y., Jin, L., Feng, L. (2015). A Cluster-Based Epidemic Model for Retweeting Trend Prediction on Micro-blog. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds) Database and Expert Systems Applications. Globe DEXA 2015 2015. Lecture Notes in Computer Science(), vol 9261. Springer, Cham. https://doi.org/10.1007/978-3-319-22849-5_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-22849-5_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22848-8
Online ISBN: 978-3-319-22849-5
eBook Packages: Computer ScienceComputer Science (R0)