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CROP: An Efficient Cross-Platform Event Popularity Prediction Model for Online Media

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Database and Expert Systems Applications (DEXA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11030))

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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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Notes

  1. 1.

    https://github.com/huaban/jieba-analysis.

  2. 2.

    https://pypi.python.org/pypi/PyWavelets/.

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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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Authors and Affiliations

  1. Shanghai Key Lab of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Mingding Liao, Xiaofeng Gao & Guihai Chen

  2. Baidu, Inc., Beijing, China

    Xuezheng Peng

Authors
  1. Mingding Liao
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  2. Xiaofeng Gao
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  3. Xuezheng Peng
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  4. Guihai Chen
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Corresponding author

Correspondence to Xiaofeng Gao .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Victoria University of Wellington, Wellington, New Zealand

    Hui Ma

  3. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  4. University of Regensburg, Regensburg, Germany

    Günther Pernul

  5. Johannes Kepler University, Linz, Austria

    Roland R. Wagner

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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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  • DOI: https://doi.org/10.1007/978-3-319-98812-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98811-5

  • Online ISBN: 978-3-319-98812-2

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