Abstract
Micro-blogging is one of the most popular social media services on which users can publish new messages (usually called tweets), submit their comments and retweet their followees’ messages. It is retweeting behavior that leading the information diffusion in a faster way. However, why some tweets are more popular than others? Whether a message will be popular in the future? These problems have attracted great attention. In this paper, we focus on predicting the popularity of a tweet on Weibo, a famous micro-blogging service in China. It is important for tremendous tasks such as breaking news detection, personalized message recommendation, advertisement placement, viral marketing etc. We propose a novel approach to predict the retweet count of a tweet by finding top-k similar tweets published by the same author. To find the top-k similar tweets we consider both content similarity and temporal similarity. Meanwhile, we also integrate our method into a classical classification method and prove our method can improve the results significantly.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Petrovic, S., Osborne, M., Lavrenko, V.: RT to Win! Predicting Message Propagation in Twitter. In: ICWSM (2011)
Kwak, H., et al.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web. ACM (2010)
Yang, J., Counts, S.: Predicting the Speed, Scale, and Range of Information Diffusion in Twitter. In: ICWSM (2010)
Hong, L., Dan, O., Davison, B.D.: Predicting popular messages in twitter. In: Proceedings of the 20th International Conference Companion on World Wide Web. ACM (2011)
Bakshy, E., et al.: Everyone’s an influencer: Quantifying influence on twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining. ACM (2011)
Boyd, D., Golder, S., Lotan, G.: Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. In: 2010 43rd Hawaii International Conference on System Sciences (HICSS). IEEE (2010)
Suh, B., et al.: Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In: 2010 IEEE Second International Conference on Social Computing (Socialcom). IEEE (2010)
Kupavskii, A., et al.: Prediction of retweet cascade size over time. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM (2012)
Khabiri, E., Hsu, C.-F., Caverlee, J.: Analyzing and Predicting Community Preference of Socially Generated Metadata: A Case Study on Comments in the Digg Community. In: ICWSM (2009)
Schumaker, R.P., Chen, H.: Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS)Â 27(2), 12 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Y., Chen, Y., Liu, T., Deng, W. (2014). Predicting the Popularity of Messages on Micro-blog Services. In: Huang, H., Liu, T., Zhang, HP., Tang, J. (eds) Social Media Processing. SMP 2014. Communications in Computer and Information Science, vol 489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45558-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-662-45558-6_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45557-9
Online ISBN: 978-3-662-45558-6
eBook Packages: Computer ScienceComputer Science (R0)