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What really matters?: characterising and predicting user engagement of news postings using multiple platforms, sentiments and topics

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  • Kholoud Khalil Aldous
  • Jisun An
  • Bernard J. Jansen

Abstract

This research characterises user engagement of approximately 3,000,000 news postings of 53 news outlets and 50,000,000 associated user comments during 8 months on 5 social media platforms (i.e. Facebook, Instagram, Twitter, YouTube, and Reddit). We investigate the effect of sentiments and topics on user engagement across four levels of user engagement expressions (i.e. views, likes, comments, cross-platform posting). We find that sentiments and topics differ by both news outlets and social media platforms, and both sentiments and topics by the four levels of user engagement expression. Finally, we predict a volume of four user engagement levels for given news content, with an 83% maximum average F1-score for the external posting of news articles from one platform to another using language and metadata features. Implications are that news outlets can benefit by developing a platform, sentiment and topic, and strategies to best achieve user engagement objectives.

Suggested Citation

  • Kholoud Khalil Aldous & Jisun An & Bernard J. Jansen, 2023. "What really matters?: characterising and predicting user engagement of news postings using multiple platforms, sentiments and topics," Behaviour and Information Technology, Taylor & Francis Journals, vol. 42(5), pages 545-568, April.
    • Handle: RePEc:taf:tbitxx:v:42:y:2023:i:5:p:545-568
    DOI: 10.1080/0144929X.2022.2030798
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    File URL: http://hdl.handle.net/10.1080/0144929X.2022.2030798
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    Cited by:

    1. Wang, Ning & Guo, Ziyu & Shang, Dawei & Li, Keyuyang, 2024. "Carbon trading price forecasting in digitalization social change era using an explainable machine learning approach: The case of China as emerging country evidence," Technological Forecasting and Social Change, Elsevier, vol. 200(C).

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