IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v11y2024i1d10.1057_s41599-023-02247-z.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-023-02247-z
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-023-02247-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yating Wen & Xiaodong Zhao & Yuqi Zang & Xingguo Li, 2024. "How the crisis of trust in experts occurs on social media in China? Multiple-case analysis based on data mining," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-18, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Olga Zagovora & Katrin Weller, 2025. "Science communicators, flat-earthers, or fitness coaches: who is citing scientific publications in youtube video descriptions?," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(1), pages 205-235, January.
    2. Qianjin Zong & Yafen Xie & Rongchan Tuo & Jingshi Huang & Yang Yang, 2019. "The impact of video abstract on citation counts: evidence from a retrospective cohort study of New Journal of Physics," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1715-1727, June.
    3. Hamid R. Jamali & Majid Nabavi & Saeid Asadi, 2018. "How video articles are cited, the case of JoVE: Journal of Visualized Experiments," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1821-1839, December.
    4. Hou, Jianhua & Wang, Yuanyuan & Zhang, Yang & Wang, Dongyi, 2022. "How do scholars and non-scholars participate in dataset dissemination on Twitter," Journal of Informetrics, Elsevier, vol. 16(1).
    5. Ho Fai Chan & Bruno S. Frey & Jana Gallus & Markus Schaffner & Benno Torgler & Stephen Whyte, 2014. "Do the best scholars attract the highest speaking fees? An exploration of internal and external influence," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 793-817, October.
    6. Yang Liu & Rana Tahir Naveed & Sara Kanwal & Muhammad Tahir Khan & Ali F Dalain & Wei Lan, 2023. "Psychology in action: Social media communication, CSR, and consumer behavior management in banking," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-23, August.
    7. Moradi, Masoud & Dass, Mayukh & Kumar, Piyush, 2023. "Differential effects of analytical versus emotional rhetorical style on review helpfulness," Journal of Business Research, Elsevier, vol. 154(C).
    8. Chau Minh Nguyen & Marcelo Vinhal Nepomuceno & Yany Grégoire & Renaud Legoux, 2024. "Striking the Right Notes: Long- and Short-Term Financial Impacts of Musicians’ Charity Advocacy Versus Other Signaling Types," Journal of Business Ethics, Springer, vol. 193(1), pages 217-233, August.
    9. Reem Mohamed Gouda & Yasser Tawfik Halim, 2025. "The role of sustainable social media content in enhancing customer loyalty in the hospitality industry," Future Business Journal, Springer, vol. 11(1), pages 1-26, December.
    10. Zhai, Li & Yan, Xiangbin, 2022. "A directed collaboration network for exploring the order of scientific collaboration," Journal of Informetrics, Elsevier, vol. 16(4).
    11. Anqi Ma & Yu Liu & Xiujuan Xu & Tao Dong, 2021. "A deep-learning based citation count prediction model with paper metadata semantic features," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6803-6823, August.
    12. Haochuan Cui & Tiewei Li & Cheng-Jun Wang, 2023. "Climbing up the ladder of abstraction: how to span the boundaries of knowledge space in the online knowledge market?," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    13. Bi, Sheng & Li, Menglin, 2025. "The effect of stress on compliance with health-related advertising," Journal of Business Research, Elsevier, vol. 193(C).
    14. Don Watson & Manfred Krug & Claus-Christian Carbon, 2022. "The relationship between citations and the linguistic traits of specific academic discourse communities identified by using social network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1755-1781, April.
    15. Laura Freeman & Abdul Rahman & Feras A. Batarseh, 2021. "Enabling Artificial Intelligence Adoption through Assurance," Social Sciences, MDPI, vol. 10(9), pages 1-15, August.
    16. Hajian, Ava & Sadeghi, Russell & Prybutok, Victor R. & Koh, Chang E., 2024. "Increasing trust and value of mobile advertising in retailing: A survey design, machine learning approach, and blockchain in the trust path," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    17. Nikolaos T. Giannakopoulos & Dimitrios P. Reklitis & Marina C. Terzi & Damianos P. Sakas & Nikos Kanellos, 2024. "Video marketing for decentralized finance platforms’ services," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 29(4), pages 1225-1259, December.
    18. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
    19. Kevin W. Boyack, 2017. "Investigating the effect of global data on topic detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 999-1015, May.
    20. Andrej Srakar, 2017. "Prevalence of Diseases and Health Care Utilization ofthe Self-Employed Artists and TheirEmpirical Determinants: Evidence From a Slovenian Survey," ACEI Working Paper Series AWP-08-2017, Association for Cultural Economics International, revised Sep 2017.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-023-02247-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.