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Scalable Detection of Viral Memes from Diffusion Patterns

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Complex Spreading Phenomena in Social Systems

Part of the book series: Computational Social Sciences ((CSS))

Abstract

Social media and social networking platforms have flourished with the rapid development of mobile technology and the ubiquitous use of the Internet. As a result, memes, or pieces of information spreading from person to person, can be reshared among users quickly and gain huge popularity. As viral memes have tremendous social and economic impact, detecting these viral memes at their early stages of spread is a worthy, yet challenging problem. Here we review the literature on predicting viral memes, and present empirical results from Twitter and Tumblr datasets. We demonstrate how diffusion patterns of memes, in the context of network communities, play an important role in predicting virality. We show that it is feasible to obtain predictive features based on community structure even at the massive scales that common social media services need to process. Our results may not only enable practitioners to make predictions about meme diffusion, but also help researchers understand how and why different factors, in particular diffusion patterns in communities, affect online virality.

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Author information

Authors and Affiliations

  1. Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University, Bloomington, IN, USA

    Pik-Mai Hui, Lilian Weng, Yong-Yeol Ahn & Filippo Menczer

  2. Yahoo!, Sunnyvale, CA, USA

    Alireza Sahami Shirazi

Authors
  1. Pik-Mai Hui
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  2. Lilian Weng
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  3. Alireza Sahami Shirazi
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  4. Yong-Yeol Ahn
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  5. Filippo Menczer
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Corresponding author

Correspondence to Pik-Mai Hui .

Editor information

Editors and Affiliations

  1. Technical University of Denmark, Lyngby, Denmark

    Sune Lehmann

  2. Indiana University, Bloomington, Indiana, USA

    Yong-Yeol Ahn

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Hui, PM., Weng, L., Sahami Shirazi, A., Ahn, YY., Menczer, F. (2018). Scalable Detection of Viral Memes from Diffusion Patterns. In: Lehmann, S., Ahn, YY. (eds) Complex Spreading Phenomena in Social Systems. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-77332-2_11

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