Abstract
Microblog has become a popular social network service. It provides a new communication platform for information acquisition, sharing and spreading. In addition to presenting daily-life reports from users, microblog also reports unexpected events, which get broad attention. How to forecast such unexpected events as early as possible? In this paper, we propose a short-term trend prediction model of topics in Sina Weibo, the most popular microblog service in China. Based on real microblog data, we first analyze which Weibo data attributes have influence on the spreading of topics, and then build a topic spreading model. Further, we develop a model of short-term trend prediction of topics. With dataset from Weibo, we test our algorithm and analyze the experimental data which shows that the proposed model can give a short-term trend prediction of Weibo topic.
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Acknowledgments
This study was supported by the National Natural Science Foundation of China (Grant No. 61202163, 61240035, 61373100); Natural Science Foundation of Shanxi Province (Grant No. 2012011015-1) and Programs for Science and Technology Development of Shanxi Province (Grant No. 20120313032-3). This work was also supported in part by the US National Science Foundation (NSF) under Grant No. CNS-1016320 and CCF-0829993.
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Zhao, J., Wu, W., Zhang, X. et al. A short-term trend prediction model of topic over Sina Weibo dataset. J Comb Optim 28, 613–625 (2014). https://doi.org/10.1007/s10878-013-9674-0
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DOI: https://doi.org/10.1007/s10878-013-9674-0