Abstract
Interaction behaviors of users on different platforms on the Internet make user-generated content spread widely and become popular. How to model and predict the popularity of topic concerned by users is vital for many fields. Aiming at the problem of topic popularity prediction on microblog platform, a popularity prediction method based on similar topics and co-occurrence topics is proposed. The method is further evaluated with the Sina Weibo dataset. The experimental results show that our method can have relatively better performance in predicting topic popularity than the baseline methods.
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Acknowledgements
The work described in this paper is partially supported by National Key Fundamental Research and Development Program (No. 2013CB329601, No. 2013CB329602, No. 2013CB329604) and National Natural Science Foundation of China (No. 61502517, No. 61372191, No. 61572492, No. 61502517), 863 Program of China (Grant No. 2012AA01A401, 2012AA01A402, 2012AA013002), Project funded by China Postdoctoral Science Foundation (2013M542560, 2015T81129).
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Deng, L., Liu, Q., Xu, J., Huang, J., Zhou, B., Jia, Y. (2018). Predicting Popularity of Topic Based on Similarity Relation and Co-occurrence Relation. In: Xhafa, F., Patnaik, S., Zomaya, A. (eds) Advances in Intelligent Systems and Interactive Applications. IISA 2017. Advances in Intelligent Systems and Computing, vol 686. Springer, Cham. https://doi.org/10.1007/978-3-319-69096-4_23
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DOI: https://doi.org/10.1007/978-3-319-69096-4_23
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