Predicting Audience Engagement Across Social Media Platforms in the News Domain

  • Kholoud Khalil AldousEmail author
  • Jisun An
  • Bernard J. Jansen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11864)


We analyze cross-platform factors for posts on both single and multiple social media platforms for numerous news outlets to better predict audience engagement, precisely the number of likes and comments. We collect 676,779 social media posts from 53 news outlets during eight months on four social media platforms (Facebook, Instagram, Twitter, and YouTube), along with the associated comments (more than 31 million) and the number of likes (more than 840 million). We develop a framework for predicting the audience engagement based on both linguistic features of the post and social media platform factors. Among other findings, results show that content with high engagement on one platform does not guarantee high engagement on another platform, even when news outlets use similar cross-platform posts; however, for some content, cross-sharing posts on a platform will increase overall audience engagement on another platform. As one of the few multiple social media platform studies, the findings have implications for the news domain, as well as other fields that distribute online content via social media.


Audience engagement News outlets Social media 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kholoud Khalil Aldous
    • 1
    Email author
  • Jisun An
    • 2
  • Bernard J. Jansen
    • 2
  1. 1.College of Science and EngineeringHamad Bin Khalifa UniversityDohaQatar
  2. 2.Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar

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