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The Missing Link Between User Engagement and Misinformation’s Impact on Online Behavior

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2022)

Abstract

By analyzing tweets sent before and after Twitter users’ first interactions with known low- or high-credibility information sources, we have observed that people who interacted with low-credibility information tended to be more hateful even before that interaction. Such people seemed to further increase their hatefulness only following particularly engaged interactions with low-credibility content. By demonstrating the importance of a person’s level of engagement with misinformation for understanding its impact, these results bridge the gap between studies that detected behavioral effects amongst believers of misinformation and research that instead either failed to detect such effects or concluded that misinformation largely affects small, predisposed audiences. Our analysis also reveals a stronger link between interaction with misinformation and change in what people discuss rather than how they write posts.

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References

  1. Au, C.H., Ho, K.K.W., Chiu, D.K.W.: The role of online misinformation and fake news in ideological polarization: barriers, catalysts, and implications. Inf. Syst. Front. 1–24 (2021). https://doi.org/10.1007/s10796-021-10133-9

  2. Bail, C.A., et al.: Assessing the Russian internet research agency’s impact on the political attitudes and behaviors of American twitter users in late 2017. Proc. Nat. Acad. Sci. 117(1), 243–250 (2019)

    Google Scholar 

  3. Barrera, O., Guriev, S., Henry, E., Zhuravskaya, E.: Facts, alternative facts, and fact checking in times of post-truth politics. J. Public Econ. 182, 104123 (2020)

    Article  Google Scholar 

  4. Beskow, D., Carley, K.: Bot-hunter: a tiered approach to detecting & characterizing automated activity on Twitter. In: 2018 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS (2018)

    Google Scholar 

  5. Cantarella, M., Fraccaroli, N., Volpe, R.: Does fake news affect voting behaviour? CEIS Working Paper No. 493 (2020)

    Google Scholar 

  6. Cinelli, M., Pelicon, A., Mozetič, I., Quattrociocchi, W., Novak, P.K., Zollo, F.: Dynamics of online hate and misinformation. Sci. Rep. 11, 22083 (2021)

    Article  Google Scholar 

  7. Greene, C.M., Murphy, G.: Quantifying the effects of fake news on behavior: evidence from a study of COVID-19 misinformation. J. Exp. Psychol. Appl. 27(4), 773–784 (2021)

    Article  Google Scholar 

  8. Greene, W.: Econometric Analysis, 7th edn. Pearson, Boston (2012)

    Google Scholar 

  9. Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., Lazer, D.: Fake news on twitter during the 2016 U.S. presidential election. Science 363(6425), 374–378 (2019)

    Google Scholar 

  10. Guess, A.M., Nyhan, B., Reifler, J.: Exposure to untrustworthy websites in the 2016 US election. Nat. Hum. Behav. 4, 472–480 (2020)

    Article  Google Scholar 

  11. Gunther, R., Beck, P.A., Nisbet, E.C.: “Fake news” and the defection of 2012 Obama voters in the 2016 presidential election. Electoral Stud. 61, 102030 (2019)

    Google Scholar 

  12. Loomba, S., de Figueiredo, A., Piatek, S.J., de Graaf, K., Larson, H.J.: Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nat. Hum. Behav. 5, 337–348 (2021)

    Article  Google Scholar 

  13. Maurer, M., Reinemann, C.: Learning versus knowing: effects of misinformation in televised debates. Commun. Res. 33(6), 489–506 (2006)

    Article  Google Scholar 

  14. Media Bias/Fact Check. https://mediabiasfactcheck.com

  15. Netanomics: NetMapper (2021). https://netanomics.com/netmapper/

  16. Ng, L.H.X., Robertson, D.C., Carley, K.M.: Stabilizing a supervised bot detection algorithm: how much data is needed for consistent predictions? Online Soc. Netw. Media 28(1), 100198 (2022)

    Article  Google Scholar 

  17. Ramalho, E., Ramalho, J., Murteira, J.: Alternative estimating and testing empirical strategies for fractional regression models. J. Econ. Surv. 25(1), 19–68 (2011)

    Article  Google Scholar 

  18. Suntwal, S., Brown, S.A., Patton, M.W.: How does information spread? a study of true and fake news. In: Proceedings of the 53rd Hawaii International Conference on System Sciences (2020)

    Google Scholar 

  19. Uyheng, J., Bellutta, D., Carley, K.M.: Bots amplify and redirect hate speech in online discourse about racism during the COVID-19 pandemic. Soc. Media Soc. 8(3) (2022). https://doi.org/10.1177/20563051221104749

  20. Uyheng, J., Carley, K.M.: Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines. J. Comput. Soc. Sci. 3, 445–468 (2020)

    Article  Google Scholar 

  21. Zimmermann, F., Kohring, M.: Mistrust, disinforming news, and vote choice: a panel survey on the origins and consequences of believing disinformation in the 2017 German parliamentary election. Polit. Commun. 37(2), 215–237 (2020)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Center for Informed Democracy and Social Cybersecurity with funding from the Knight Foundation and Cognizant. Additional support was given by the Center for Computational Analysis of Social and Organizational Systems. The views and conclusions contained herein belong to the authors and should not be interpreted as representing the official policies of the Knight Foundation, Cognizant, or the U.S. government.

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Correspondence to Daniele Bellutta .

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Bellutta, D., Uyheng, J., Carley, K.M. (2022). The Missing Link Between User Engagement and Misinformation’s Impact on Online Behavior. In: Thomson, R., Dancy, C., Pyke, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2022. Lecture Notes in Computer Science, vol 13558. Springer, Cham. https://doi.org/10.1007/978-3-031-17114-7_8

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  • DOI: https://doi.org/10.1007/978-3-031-17114-7_8

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