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Toward early and order-of-magnitude cascade prediction in social networks

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Abstract

When a piece of information (microblog, photograph, video, link, etc.) starts to spread in a social network, an important question arises: will it spread to “viral” proportions—where “viral” can be defined as an order-of-magnitude increase. However, several previous studies have established that cascade size and frequency are related through a power law—which leads to a severe imbalance in this classification problem. In this paper, we devise a suite of measurements based on “structural diversity”—the variety of social contexts (communities) in which individuals partaking in a given cascade engage. We demonstrate these measures are able to distinguish viral from non-viral cascades, despite the severe imbalance of the data for this problem. Further, we leverage these measurements as features in a classification approach, successfully predicting microblogs that grow from 50 to 500 reposts with precision of 0.69 and recall of 0.52 for the viral class—despite this class comprising under 2 % of samples. This significantly outperforms our baseline approach as well as the current state of the art. We also show this approach also performs well for identifying whether cascades observed for 60 min will grow to 500 reposts as well as demonstrate how we can trade-off between precision and recall.

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Notes

  1. http://www.wise2012.cs.ucy.ac.cy/challenge.html.

  2. http://weibo.com.

  3. This was their highest-performing set of features for predicting cascades that grew from 50 to 367 and 100 to 417 reposts. We also included the baseline feature in this set as we found it improved the effectiveness of this approach.

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Acknowledgments

Some of the authors of this paper are supported by AFOSR Young Investigator Program (YIP) Grant FA9550-15-1-0159, ARO Grant W911NF-15-1-0282, and the DoD Minerva program. Portions of this work were also disclosed in US provisional Patent 62/201, 517. A non-provisional patent is currently being filed.

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Authors and Affiliations

  1. Arizona State University, Tempe, AZ, USA

    Ruocheng Guo, Elham Shaabani, Abhinav Bhatnagar & Paulo Shakarian

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  1. Ruocheng Guo
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  2. Elham Shaabani
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  3. Abhinav Bhatnagar
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  4. Paulo Shakarian
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Correspondence to Ruocheng Guo.

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Guo, R., Shaabani, E., Bhatnagar, A. et al. Toward early and order-of-magnitude cascade prediction in social networks. Soc. Netw. Anal. Min. 6, 64 (2016). https://doi.org/10.1007/s13278-016-0372-7

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  • DOI: https://doi.org/10.1007/s13278-016-0372-7

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