A real-time method to predict social media popularity

Xiao Chen () and Zhe-Ming Lu
Additional contact information
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

International Journal of Modern Physics C (IJMPC), 2017, vol. 28, issue 12, 1-9

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.

Keywords: Online social network; cascade prediction; feature extraction (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0129183117501443
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijmpcx:v:28:y:2017:i:12:n:s0129183117501443

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0129183117501443

Access Statistics for this article

International Journal of Modern Physics C (IJMPC) is currently edited by H. J. Herrmann

More articles in International Journal of Modern Physics C (IJMPC) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().