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Resource Management for Big Data Platforms pp 419–439Cite as

Predicting Video Virality on Twitter

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Part of the book series: Computer Communications and Networks ((CCN))

Abstract

Our work merges user-centric data from Twitter with video-centric data from YouTube to investigate the ties between predictability of video sharing and the social context of video uploaders. It provides a combination of social media datasets, giving insights than neither dataset (social network and media service) individually gives. We develop an accurate model to predict future popularity of a video resource given features of the underlying network of its initial sharer. The set of features we propose and analyze are based on the notion of influence score of a user and its fluctuation through time, as well as the distance of content interests among users for both datasets. We discover that the latter feature plays an important role in video popularity prediction, suggesting high dependence of video sharing via Twitter on the video content itself. We proceed to incorporate our prediction model into a mechanism for content delivery, achieving substantial improvement of the user experience.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Cyprus, 1 University Avenue, P.O. Box 20537, 2109, Nicosia, Cyprus

    Irene Kilanioti & George A. Papadopoulos

Authors
  1. Irene Kilanioti
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  2. George A. Papadopoulos
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Corresponding authors

Correspondence to Irene Kilanioti or George A. Papadopoulos .

Editor information

Editors and Affiliations

  1. University Politehnica of Bucharest, Bucharest, Romania

    Florin Pop

  2. Cracow University of Technology, Cracow, Poland

    Joanna Kołodziej

  3. Second University of Naples, Naples, Caserta, Italy

    Beniamino Di Martino

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Kilanioti, I., Papadopoulos, G.A. (2016). Predicting Video Virality on Twitter. In: Pop, F., Kołodziej, J., Di Martino, B. (eds) Resource Management for Big Data Platforms. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-44881-7_20

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  • DOI: https://doi.org/10.1007/978-3-319-44881-7_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44880-0

  • Online ISBN: 978-3-319-44881-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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