Predicting Video Virality on Twitter

  • Irene KilaniotiEmail author
  • George A. PapadopoulosEmail author
Part of the Computer Communications and Networks book series (CCN)


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.


Analytics Big Data in Social Networks Social Cascade Social Prediction Regression Analysis Video Popularity 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of CyprusNicosiaCyprus

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