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News source bias and sentiment on social media

Author

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  • Brian Knutson
  • Tiffany W Hsu
  • Michael Ko
  • Jeanne L Tsai

Abstract

As social media becomes a key channel for news consumption and sharing, proliferating partisan and mainstream news sources must increasingly compete for users’ attention. While affective qualities of news content may promote engagement, it is not clear whether news source bias influences affective content production or virality, or whether any differences have changed over time. We analyzed the sentiment of ~30 million posts (on twitter.com) from 182 U.S. news sources that ranged from extreme left to right bias over the course of a decade (2011–2020). Biased news sources (on both left and right) produced more high arousal negative affective content than balanced sources. High arousal negative content also increased reposting for biased versus balanced sources. The combination of increased prevalence and virality for high arousal negative affective content was not evident for other types of affective content. Over a decade, the virality of high arousal negative affective content also increased, particularly in balanced news sources, and in posts about politics. Together, these findings reveal that high arousal negative affective content may promote the spread of news from biased sources, and conversely imply that sentiment analysis tools might help social media users to counteract these trends.

Suggested Citation

  • Brian Knutson & Tiffany W Hsu & Michael Ko & Jeanne L Tsai, 2024. "News source bias and sentiment on social media," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-17, October.
    • Handle: RePEc:plo:pone00:0305148
    DOI: 10.1371/journal.pone.0305148
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    References listed on IDEAS

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    1. M. J. Crockett, 2017. "Moral outrage in the digital age," Nature Human Behaviour, Nature, vol. 1(11), pages 769-771, November.
    2. Osmundsen, Mathias & Bor, Alexander & Vahlstrup, Peter Bjerregaard & Bechmann, Anja & Petersen, Michael Bang, 2021. "Partisan Polarization Is the Primary Psychological Motivation behind Political Fake News Sharing on Twitter," American Political Science Review, Cambridge University Press, vol. 115(3), pages 999-1015, August.
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    1. Li, Yulei & Cao, Dongmei & Hewitt, Michael & Shan, Shan, 2026. "Using large-language models to analyse social media engagement with the public sector communication on circular economy: Evidence from X," Technological Forecasting and Social Change, Elsevier, vol. 222(C).

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