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The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions

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  • Carlos Carrasco-Farré

    (Universitat Ramon Llull—ESADE Business School)

Abstract

Not all misinformation is created equal. It can adopt many different forms like conspiracy theories, fake news, junk science, or rumors among others. However, most of the existing research does not account for these differences. This paper explores the characteristics of misinformation content compared to factual news—the “fingerprints of misinformation”—using 92,112 news articles classified into several categories: clickbait, conspiracy theories, fake news, hate speech, junk science, and rumors. These misinformation categories are compared with factual news measuring the cognitive effort needed to process the content (grammar and lexical complexity) and its emotional evocation (sentiment analysis and appeal to morality). The results show that misinformation, on average, is easier to process in terms of cognitive effort (3% easier to read and 15% less lexically diverse) and more emotional (10 times more relying on negative sentiment and 37% more appealing to morality). This paper is a call for more fine-grained research since these results indicate that we should not treat all misinformation equally since there are significant differences among misinformation categories that are not considered in previous studies.

Suggested Citation

  • Carlos Carrasco-Farré, 2022. "The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-18, December.
    • Handle: RePEc:pal:palcom:v:9:y:2022:i:1:d:10.1057_s41599-022-01174-9
    DOI: 10.1057/s41599-022-01174-9
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    1. Alessandro Bessi & Mauro Coletto & George Alexandru Davidescu & Antonio Scala & Guido Caldarelli & Walter Quattrociocchi, 2015. "Science vs Conspiracy: Collective Narratives in the Age of Misinformation," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-17, February.
    2. Monika Taddicken & Laura Wolff, 2020. "‘Fake News’ in Science Communication: Emotions and Strategies of Coping with Dissonance Online," Media and Communication, Cogitatio Press, vol. 8(1), pages 206-217.
    3. Kulkarni, Kalpak K. & Kalro, Arti D. & Sharma, Dinesh & Sharma, Piyush, 2020. "A typology of viral ad sharers using sentiment analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    4. Sahil Loomba & Alexandre Figueiredo & Simon J. Piatek & Kristen Graaf & Heidi J. Larson, 2021. "Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA," Nature Human Behaviour, Nature, vol. 5(3), pages 337-348, March.
    5. Liu, Zhiwei & Park, Sangwon, 2015. "What makes a useful online review? Implication for travel product websites," Tourism Management, Elsevier, vol. 47(C), pages 140-151.
    6. Dietram A. Scheufele & Nicole M. Krause, 2019. "Science audiences, misinformation, and fake news," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(16), pages 7662-7669, April.
    7. Hunt Allcott & Matthew Gentzkow & Chuan Yu, 2018. "Trends in the Diffusion of Misinformation on Social Media," Papers 1809.05901, arXiv.org.
    8. J. A. Hartigan & M. A. Wong, 1979. "A K‐Means Clustering Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 100-108, March.
    9. Alexandre Bovet & Hernán A. Makse, 2019. "Influence of fake news in Twitter during the 2016 US presidential election," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    10. Lisa Singh & Leticia Bode & Ceren Budak & Kornraphop Kawintiranon & Colton Padden & Emily Vraga, 2020. "Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams," Journal of Computational Social Science, Springer, vol. 3(2), pages 343-366, November.
    11. Bence Bago & David Rand & Gordon Pennycook, 2020. "Fake news, fast and slow: Deliberation reduces belief in false (but not true) news headlines," Post-Print hal-03477497, HAL.
    12. Marcella Tambuscio & Diego F. M. Oliveira & Giovanni Luca Ciampaglia & Giancarlo Ruffo, 2018. "Network segregation in a model of misinformation and fact-checking," Journal of Computational Social Science, Springer, vol. 1(2), pages 261-275, September.
    13. Cynthia Van Hee & Gilles Jacobs & Chris Emmery & Bart Desmet & Els Lefever & Ben Verhoeven & Guy De Pauw & Walter Daelemans & Véronique Hoste, 2018. "Automatic detection of cyberbullying in social media text," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-22, October.
    14. Ragini, J. Rexiline & Anand, P.M. Rubesh & Bhaskar, Vidhyacharan, 2018. "Big data analytics for disaster response and recovery through sentiment analysis," International Journal of Information Management, Elsevier, vol. 42(C), pages 13-24.
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