Abstract
Recently, retweeting is found to be an important action to understand diffusion in microblogging sites. There have been studies on how tweets propagate in networks. Previous studies have shown that history of users interaction and properties of the message are good attributes to understand the retweet behavior of users. Factors like content of message and time are less investigated. We propose a model for predicting users who are more likely to retweet a particular tweet using tweet properties, time and estimates of pairwise influence among users. We have analyzed retweet cascades and validated that structural, social, behavioral and history of nodes are equally important for influence estimation among users. We develop a model which ranks the users based on the likelihood of the users to be potential retweeters. We have performed experiments on real world Twitter sub-graphs and our results validate our proposed work satisfactorily. We have also compared our results with existing works and our results outperform them.
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Rangnani, S., Devi, V.S. (2016). Predicting Potential Retweeters for a Microblog on Twitter. In: Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., Chan, J. (eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-27000-5_14
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DOI: https://doi.org/10.1007/978-3-319-27000-5_14
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