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The processing and evaluation of news content on social media is influenced by peer-user commentary

Author

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  • Arnout B. Boot

    (Erasmus University Rotterdam)

  • Katinka Dijkstra

    (Erasmus University Rotterdam)

  • Rolf A. Zwaan

    (Erasmus University Rotterdam)

Abstract

Contemporary news often spreads via social media. This study investigated whether the processing and evaluation of online news content can be influenced by Likes and peer-user comments. An online experiment was designed, using a custom-built website that resembled Facebook, to explore how Likes, positive comments, negative comments, or a combination of positive and negative comments would affect the reader’s processing of news content. The results showed that especially negative comments affected the readers’ personal opinions about the news content, even in combination with other positive comments: They (1) induced more negative attitudes, (2) lowered intent to share it, (3) reduced agreement with conveyed ideas, (4) lowered perceived attitude of the general public, and (5) decreased the credibility of the content. Against expectations, the presence of Likes did not affect the readers, irrespective of the news content. An important consideration is that, while the negative comments were persuasive, they comprised subjective, emotive, and fallacious rhetoric. Finally, negativity bias, the perception of expert authority, and cognitive heuristics are discussed as potential explanations for the persuasive effect of negative comments.

Suggested Citation

  • Arnout B. Boot & Katinka Dijkstra & Rolf A. Zwaan, 2021. "The processing and evaluation of news content on social media is influenced by peer-user commentary," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-11, December.
    • Handle: RePEc:pal:palcom:v:8:y:2021:i:1:d:10.1057_s41599-021-00889-5
    DOI: 10.1057/s41599-021-00889-5
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    References listed on IDEAS

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    1. Amanda Hinnant & Roma Subramanian & Rachel Young, 2016. "User comments on climate stories: impacts of anecdotal vs. scientific evidence," Climatic Change, Springer, vol. 138(3), pages 411-424, October.
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    Cited by:

    1. Guanghui Wang & Yushan Wang & Kaidi Liu & Shu Sun, 2024. "A classification and recognition algorithm of key figures in public opinion integrating multidimensional similarity and K-shell based on supernetwork," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-19, December.

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