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Affect in science communication: a data-driven analysis of TED Talks on YouTube

Author

Listed:
  • Olivia Fischer

    (University of Zurich)

  • Loris T. Jeitziner

    (University of Applied Sciences and Arts Northwestern Switzerland
    University of Basel)

  • Dirk U. Wulff

    (University of Basel
    Max Planck Institute for Human Development)

Abstract

Science communication is evolving: Increasingly, it is directed at the public rather than academic peers. Understanding the circumstances under which the public engages with scientific content is therefore crucial to improving science communication. In this article, we investigate the role of affect on audience engagement with a modern form of science communication: TED Talks on the social media platform YouTube. We examined how two aspects of affect, valence and density are associated with public engagement with the talk in terms of popularity (reflecting views and likes) and polarity (reflecting dislikes and comments). We found that the valence of TED Talks was associated with both popularity and polarity: Positive valence was linked to higher talk popularity and lower talk polarity. Density, on the other hand, was only associated with popularity: Higher affective density was linked to higher popularity—even more so than valence—but not polarity. Moreover, the association between affect and engagement was moderated by talk topic, but not by whether the talk included scientific content. Our results establish affect as an important covariate of audience engagement with scientific content on social media, which science communicators may be able to leverage to steer engagement and increase reach.

Suggested Citation

  • Olivia Fischer & Loris T. Jeitziner & Dirk U. Wulff, 2024. "Affect in science communication: a data-driven analysis of TED Talks on YouTube," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.
  • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-023-02247-z
    DOI: 10.1057/s41599-023-02247-z
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    References listed on IDEAS

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    1. Cassidy R. Sugimoto & Mike Thelwall, 2013. "Scholars on soap boxes: Science communication and dissemination in TED videos," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(4), pages 663-674, April.
    2. Andrea Fronzetti Colladon & Ciriaco Andrea D’Angelo & Peter A. Gloor, 2020. "Predicting the future success of scientific publications through social network and semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 357-377, July.
    3. Jonas M. B. Haslbeck & Dirk U. Wulff, 2020. "Estimating the number of clusters via a corrected clustering instability," Computational Statistics, Springer, vol. 35(4), pages 1879-1894, December.
    4. Cassidy R. Sugimoto & Mike Thelwall, 2013. "Scholars on soap boxes: Science communication and dissemination in TED videos," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(4), pages 663-674, April.
    5. Kate MacKrill & Connor Silvester & James W. Pennebaker & Keith J. Petrie, 2021. "What makes an idea worth spreading? Language markers of popularity in TED talks by academics and other speakers," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 1028-1038, August.
    6. Melanie Schreiner & Thomas Fischer & Rene Riedl, 2021. "Impact of content characteristics and emotion on behavioral engagement in social media: literature review and research agenda," Electronic Commerce Research, Springer, vol. 21(2), pages 329-345, June.
    7. Scott Emmons & Stephen Kobourov & Mike Gallant & Katy Börner, 2016. "Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-18, July.
    8. Fedric Kujur & Saumya Singh, 2018. "Emotions as predictor for consumer engagement in YouTube advertisement," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 15(2), pages 184-197, March.
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