Abstract
The popularity analysis of social media is crucial for monitoring the spread of information, which is beneficial to public concerns track and decision-making for online platforms. Numerous studies concentrate on the trend analysis on single platform, but they neglect the data correlation between different platforms. In this paper, we propose CROP, a cross-platform event popularity prediction model to forecast the popularity of events on one platform based on the information of the auxiliary platform. We first define the cross-platform event popularity prediction problem. Then we clean the data and explore the slot matching of event time series in diverse platforms. Moreover, we first define the aggregated popularity for the feature construction of event popularity prediction model. Finally, extensive experiments based on events data show that CROP achieves great improvement for predicting accuracy over other baseline approaches.
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Acknowledgement
This work is supported by the program of International S&T Cooperation (2016YFE0100300), the China 973 project (2014CB340303), the National Natural Science Foundation of China (Grant number 61472252, 61672353), the Shanghai Science and Technology Fund (Grant number 17510740200), and CCF-Tencent Open Research Fund (RAGR20170114).
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Liao, M., Gao, X., Peng, X., Chen, G. (2018). CROP: An Efficient Cross-Platform Event Popularity Prediction Model for Online Media. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11030. Springer, Cham. https://doi.org/10.1007/978-3-319-98812-2_3
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