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A real-time method to predict social media popularity

Author

Listed:
  • Xiao Chen

    (School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, P. R. China)

  • Zhe-Ming Lu

    (School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, P. R. China)

Abstract

How to predict the future popularity of a message or video on online social media (OSM) has long been an attractive problem for researchers. Although many difficulties are still ahead, recent studies suggest that temporal and topological features of early adopters generally play a very important role. However, with the increase of the adopters, the feature space will grow explosively. How to select the most effective features is still an open issue. In this work, we investigate several feature extraction methods over the Twitter platform and find that most predictive power concentrates on the second half of the propagation period, and that not only a model trained on one platform generalizes well to others as previous works observed, but also a model trained on one dataset performs well on predicting the popularity for other datasets with different number of observed early adopters. According to these findings, at least for the best features by far, the data used to extract features can be halved without loss of evident accuracy and we provide a way to roughly predict the growth trend of a social-media item in real-time.

Suggested Citation

  • Xiao Chen & Zhe-Ming Lu, 2017. "A real-time method to predict social media popularity," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 28(12), pages 1-9, December.
    • Handle: RePEc:wsi:ijmpcx:v:28:y:2017:i:12:n:s0129183117501443
    DOI: 10.1142/S0129183117501443
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