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Emotional Dynamics in the Age of Misinformation

Author

Listed:
  • Fabiana Zollo
  • Petra Kralj Novak
  • Michela Del Vicario
  • Alessandro Bessi
  • Igor Mozetič
  • Antonio Scala
  • Guido Caldarelli
  • Walter Quattrociocchi

Abstract

According to the World Economic Forum, the diffusion of unsubstantiated rumors on online social media is one of the main threats for our society. The disintermediated paradigm of content production and consumption on online social media might foster the formation of homogeneous communities (echo-chambers) around specific worldviews. Such a scenario has been shown to be a vivid environment for the diffusion of false claim. Not rarely, viral phenomena trigger naive (and funny) social responses—e.g., the recent case of Jade Helm 15 where a simple military exercise turned out to be perceived as the beginning of the civil war in the US. In this work, we address the emotional dynamics of collective debates around distinct kinds of information—i.e., science and conspiracy news—and inside and across their respective polarized communities. We find that for both kinds of content the longer the discussion the more the negativity of the sentiment. We show that comments on conspiracy posts tend to be more negative than on science posts. However, the more the engagement of users, the more they tend to negative commenting (both on science and conspiracy). Finally, zooming in at the interaction among polarized communities, we find a general negative pattern. As the number of comments increases—i.e., the discussion becomes longer—the sentiment of the post is more and more negative.

Suggested Citation

  • Fabiana Zollo & Petra Kralj Novak & Michela Del Vicario & Alessandro Bessi & Igor Mozetič & Antonio Scala & Guido Caldarelli & Walter Quattrociocchi, 2015. "Emotional Dynamics in the Age of Misinformation," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-22, September.
  • Handle: RePEc:plo:pone00:0138740
    DOI: 10.1371/journal.pone.0138740
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    References listed on IDEAS

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    1. Michael J. Magro, 2012. "A Review of Social Media Use in E-Government," Administrative Sciences, MDPI, vol. 2(2), pages 1-14, April.
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    Cited by:

    1. Bessi, Alessandro, 2017. "On the statistical properties of viral misinformation in online social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 459-470.
    2. Nicholas Francis Havey, 2020. "Partisan public health: how does political ideology influence support for COVID-19 related misinformation?," Journal of Computational Social Science, Springer, vol. 3(2), pages 319-342, November.
    3. Igor Mozetič & Miha Grčar & Jasmina Smailović, 2016. "Multilingual Twitter Sentiment Classification: The Role of Human Annotators," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-26, May.
    4. Tea Golob & Matej Makarovič, 2022. "Meta-Reflexivity as a Way toward Responsible and Sustainable Behavior," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    5. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    6. Pnina Fichman & Matthew Vaughn, 2021. "The relationships between misinformation and outrage trolling tactics on two Yahoo! Answers categories," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(12), pages 1498-1510, December.
    7. Bartosz Wilczek, 2018. "Media use and life satisfaction: the moderating role of social events," International Review of Economics, Springer;Happiness Economics and Interpersonal Relations (HEIRS), vol. 65(2), pages 157-184, June.
    8. Mateusz J. Kuczabski, 2022. "Fake News as a Threat to Social Resilience," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 765-782.
    9. Oestereich, André L. & Crokidakis, Nuno & Cajueiro, Daniel O., 2022. "Impact of memory and bias in kinetic exchange opinion models on random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    10. Giacomo Aletti & Irene Crimaldi & Fabio Saracco, 2021. "A model for the Twitter sentiment curve," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-28, April.
    11. Claire E. Robertson & Nicolas Pröllochs & Kaoru Schwarzenegger & Philip Pärnamets & Jay J. Bavel & Stefan Feuerriegel, 2023. "Negativity drives online news consumption," Nature Human Behaviour, Nature, vol. 7(5), pages 812-822, May.
    12. Jon Roozenbeek & Sander Linden, 2019. "Fake news game confers psychological resistance against online misinformation," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-10, December.

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