CoChat
Sign Up
Cited by 14Misinformation and Its ImpactsDeception detection and forensic psychologyCommunication and COVID-19 Impact

Emotions in misinformation studies: distinguishing affective state from emotional response and misinformation recognition from acceptance

Open access
Lühring, Jula, Shetty, Apeksha, Koschmieder, Corinna, Garcia, David, Waldherr, Annie, Metzler, Hannah·2024·Cognitive Research: Principles and Implications
DOI 10.1186/s41235-024-00607-0PMC PMC11656008
Open this paper in CoChat — free accountOpen PDF

Abstract

Prior studies indicate that emotions, particularly high-arousal emotions, may elicit rapid intuitive thinking, thereby decreasing the ability to recognize misinformation. Yet, few studies have distinguished prior affective states from emotional reactions to false news, which could influence belief in falsehoods in different ways. Extending a study by Martel et al. (Cognit Res: Principles Implic 5: 1–20, 2020), we conducted a pre-registered online survey experiment in Austria (N = 422), investigating associations of emotions and discernment of false and real news related to COVID-19. We found no associations of prior affective state with discernment, but observed higher anger and less joy in response to false compared to real news. Exploratory analyses, including automated analyses of open-ended text responses, suggested that anger arose for different reasons in different people depending on their prior beliefs. In our educated and left-leaning sample, higher anger was often related to recognizing the misinformation as such, rather than accepting the false claims. We conclude that studies need to distinguish between prior affective state and emotional response to misinformation and consider individuals’ prior beliefs as determinants of emotions.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s41235-024-00607-0

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • This lack of transparency inhibits informed consent and amplifies the potential for unintended relational harm, particularly among vulnerable populations.

    “This lack of transparency inhibits informed consent and amplifies the potential for unintended relational harm, particularly among vulnerable populations”

    From Toward an Ethic of Synthetic Relationality: Identity, Intimacy, and Risk in AI-Mediated Roleplay Environments (2025) · cites this paper as Lühring et al. 2024

    The Methods (Design and Pre-registration and deviations; page unavailable) concern an online survey of emotions and COVID-19 misinformation. The Discussion (page unavailable) addresses misinformation discernment, not whether a lack of transparency inhibits informed consent or causes relational harm. Searches for transparency, informed consent, relational harm, and vulnerable populations found no passage addressing the claim. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 39

  1. Bago, B., Rand, D. G., & Pennycook, G. (2020). Fake news, fast and slow: Deliberation reduces belief in false (but not true) news headlines. Journal of Experimental Psychology: General, 149 (8), 1608–1613. 10.1037/xge0000729 31916834 · DOI · Paper page
  2. Bago, B., Rosenzweig, L. R., Berinsky, A. J., & Rand, D. G. (2022). Emotion may predict susceptibility to fake news but emotion regulation does not seem to help. Cognition and Emotion, 36 (6), 1166–1180. 10.1080/02699931.2022.2090318 35749076 · DOI
  3. Berger, J., & Milkman, K. L. (2013). Emotion and virality: What makes online content go viral? Marketing Intelligence Review, 5 (1), 18–23. 10.2478/gfkmir-2014-0022 · DOI · Paper page
  4. Bodenhausen, G. V., Sheppard, L. A., & Kramer, G. P. (1994). Negative affect and social judgment: The differential impact of anger and sadness. European Journal of Social Psychology, 24 (1), 45–62. 10.1002/ejsp.2420240104 · DOI
  5. Boyer, M. M. (2021). Aroused argumentation: How the news exacerbates motivated reasoning. The International Journal of Press/politics. 10.1177/19401612211010577 · DOI
  6. Brader, T., Valentino, N. A., & Suhay, E. (2008). What triggers public opposition to immigration? Anxiety, group cues, and immigration threat. American Journal of Political Science, 52 (4), 959–978. 10.1111/j.1540-5907.2008.00353.x · DOI · Paper page
  7. Breyer, B., & Bluemke, M. (2016). Deutsche version der positive and negative affect schedule PANAS (GESIS Panel). Zusammenstellung Sozialwissenschaftlicher Items und Skalen (ZIS). 10.6102/ZIS242 · DOI
  8. DeSteno, D., Petty, R. E., Rucker, D. D., Wegener, D. T., & Braverman, J. (2004). Discrete emotions and persuasion: The role of emotion-induced expectancies. Journal of Personality and Social Psychology, 86 (1), 43–56. 10.1037/0022-3514.86.1.43 14717627 · DOI
  9. Dezecache, G., & Mercier, H. (2022). Emotional Vigilance. In The Oxford Handbook of Evolution and the Emotions. Oxford University Press. https://hal.science/hal-03895200
  10. Freiling, I., Krause, N. M., Scheufele, D. A., & Brossard, D. (2021). Believing and sharing misinformation, fact-checks, and accurate information on social media: The role of anxiety during COVID-19. New Media & Society. 10.1177/14614448211011451 PMC9805917 36620434 · DOI
  11. Gallagher, R. J., Frank, M. R., Mitchell, L., Schwartz, A. J., Reagan, A. J., Danforth, C. M., & Dodds, P. S. (2021). Generalized word shift graphs: A method for visualizing and explaining pairwise comparisons between texts. EPJ Data Science. 10.1140/epjds/s13688-021-00260-3 · DOI · Paper page
  12. Greenstein, M., & Franklin, N. (2020). Anger increases susceptibility to misinformation. Experimental Psychology, 67 (3), 202–209. 10.1027/1618-3169/a000489 32900296 · DOI
  13. Han, J., Cha, M., & Lee, W. (2020). Anger contributes to the spread of COVID-19 misinformation. Harvard Kennedy School Misinformation Review. 10.37016/mr-2020-39 · DOI
  14. Hasell, A., & Weeks, B. E. (2016). Partisan provocation: The role of partisan news use and emotional responses in political information sharing in social media: partisan news, emotions, and information sharing. Human Communication Research, 42 (4), 641–661. 10.1111/hcre.12092 · DOI · Paper page
  15. Holland, R. W., de Vries, M., Hermsen, B., & van Knippenberg, A. (2012). Mood and the attitude-behavior link: The happy act on impulse, the sad think twice. Social Psychological and Personality Science, 3 (3), 356–364. 10.1177/1948550611421635 · DOI
  16. Imhoff, R., & Lamberty, P. (2020). A bioweapon or a hoax? The link between distinct conspiracy beliefs about the coronavirus disease (COVID-19) outbreak and pandemic behavior. Social Psychological and Personality Science, 11 (8), 1110–1118. 10.1177/1948550620934692 38602949 PMC7342934 · DOI
  17. Kaplan, R. L., Van Damme, I., Levine, L. J., & Loftus, E. F. (2016). Emotion and false memory. Emotion Review, 8 (1), 8–13. 10.1177/1754073915601228 · DOI · Paper page
  18. Kim, H., Park, K., & Schwarz, N. (2010). Will this trip really be exciting? The role of incidental emotions in product evaluation. Journal of Consumer Research, 36 (6), 983–991. 10.1086/644763 · DOI
  19. Knobloch-Westerwick, S., Mothes, C., & Polavin, N. (2020). Confirmation bias, ingroup bias, and negativity bias in selective exposure to political information. Communication Research, 47 (1), 104–124. 10.1177/0093650217719596 · DOI · Paper page
  20. MacKuen, M., Marcus, G., Neuman, W. R., & Miller, P. R. (2010). Affective Intelligence or Personality? State vs. Trait Influences on Citizens’ Use of Political Information (SSRN Scholarly Paper 1643468). https://papers.ssrn.com/abstract=1643468
  21. Martel, C., Pennycook, G., & Rand, D. G. (2020). Reliance on emotion promotes belief in fake news. Cognitive Research: Principles and Implications, 5, 1–20. 10.1186/s41235-020-00252-3 33026546 PMC7539247 · DOI · Paper page
  22. Mather, M., & Sutherland, M. R. (2011). Arousal-biased competition in perception and memory. Perspectives on Psychological Science, 6 (2), 114–133. 10.1177/1745691611400234 21660127 PMC3110019 · DOI
  23. McPhetres, J. (2020). What should a preregistration contain? [Preprint]. PsyArXiv.10.31234/osf.io/cj5mh · DOI
  24. Meier, T., Boyd, R., Pennebaker, J., Mehl, M., Martin, M., Wolf, M., Horn, A., Meier, T., Boyd, R., Mehl, J., Martin, M., Wolf, M., & Horn, M. (2019). “LIWC auf Deutsch”: The Development, Psychometrics, and Introduction of DE-LIWC2015.10.31234/osf.io/uq8zt · DOI
  25. Mercier, H. (2017). How gullible are we? A review of the evidence from psychology and social science. Review of General Psychology, 21 (2), 103–122. 10.1037/gpr0000111 · DOI
  26. Mercier, H. (2021). How good are we at evaluating communicated information? Royal Institute of Philosophy Supplements, 89, 257–272. 10.1017/S1358246121000096 · DOI
  27. Mohammad, S. (2018). Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 174–184.10.18653/v1/P18-1017 · DOI
  28. Pröllochs, N., Bär, D., & Feuerriegel, S. (2021). Emotions in online rumor diffusion. EPJ Data Science, 10 (1), 51. 10.1140/epjds/s13688-021-00307-5 · DOI · Paper page
  29. Robertson, C. E., Pretus, C., Rathje, S., Harris, E., & Van Bavel, J. J. (2022). How social identity shapes conspiratorial belief. Current Opinion in Psychology. 10.1016/j.copsyc.2022.101423 35987090 · DOI · Paper page
  30. Roozenbeek, J., Schneider, C. R., Dryhurst, S., Kerr, J., Freeman, A. L. J., Recchia, G., van der Bles, A. M., & van der Linden, S. (2020). Susceptibility to misinformation about COVID-19 around the world. Royal Society Open Science, 7 (10), 201199. 10.1098/rsos.201199 33204475 PMC7657933 · DOI · Paper page
  31. Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39 (6), 1161–1178. 10.1037/h0077714 · DOI · Paper page
  32. Schwarz, N. (2001). Feelings as information: Implications for affective influences on information processing. In L. L. Martin & G. L. Clore (Eds.), Theories of mood and cognition: A user’s guidebook (pp. 159–176).
  33. Van Boven, L., White, K., & Huber, M. (2009). Immediacy bias in emotion perception: Current emotions seem more intense than previous emotions. Journal of Experimental Psychology: General, 138, 368–382. 10.1037/a0016074 19653796 · DOI
  34. Van Damme, I., & Smets, K. (2014). The power of emotion versus the power of suggestion: Memory for emotional events in the misinformation paradigm. Emotion, 14 (2), 310–320. 10.1037/a0034629 24219394 · DOI
  35. Walter, N., & Murphy, S. T. (2018). How to unring the bell: A meta-analytic approach to correction of misinformation. Communication Monographs, 85 (3), 423–441. 10.1080/03637751.2018.1467564 · DOI · Paper page
  36. Weeks, B. E. (2015). Emotions, partisanship, and misperceptions: How anger and anxiety moderate the effect of partisan bias on susceptibility to political misinformation: emotions and misperceptions. Journal of Communication, 65 (4), 699–719. 10.1111/jcom.12164 · DOI · Paper page
  37. Wischnewski, M., & Krämer, N. (2021). The role of emotions and identity-protection cognition when processing (Mis) information. Technology, Mind, and Behavior,. 10.1037/tmb0000029 · DOI
  38. Yeo, S. K., & McKasy, M. (2021). Emotion and humor as misinformation antidotes. Proceedings of the National Academy of Sciences. 10.1073/pnas.2002484118 PMC8054012 33837148 · DOI
  39. Zollo, F., Novak, P. K., Vicario, M. D., Bessi, A., Mozetič, I., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015). Emotional dynamics in the age of misinformation. PLoS ONE, 10 (9), e0138740. 10.1371/journal.pone.0138740 26422473 PMC4589395 · DOI · Paper page

Cited by

Sorted by citations

Authors
Lühring, J., Shetty, A., Koschmieder, C., Garcia, D., Waldherr, A., & Metzler, H.
Affiliations
  • University of Vienna (AT)
  • University of Graz (AT)
  • Institute for Globally Distributed Open Research and Education (SE)
Corresponding author
  • Lühring, Jula
  • Shetty, Apeksha
ORCID
Venue
Cognitive Research: Principles and Implications
Publisher
Springer Science and Business Media LLC
Published
2024-12-18
Volume
9
Issue
1
Pages
82-82
ISSN
2365-7464 (ISSN-L)
DOI
10.1186/s41235-024-00607-0
PMC
PMC11656008
OpenAlex
W4405528931
Cited by
14
References
8
Citations by year
2026: 10 · 2025: 4
FWCI
10.04
Type
journal article
Language
en
Open access
Licence
Locations
  • Cognitive Research Principles and Implications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • KOPS (University of Konstanz) (submittedVersion, cc-by)
Funders
  • Vienna Science and Technology Fund (10.47379/ICT20028, 10.47379/VRG16005)
  • University of Vienna
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Psychology › Social Psychology › Deception detection and forensic psychology
  • Social Sciences › Communication › Communication and COVID-19 Impact
Keywords
misinformation, emotions, fake news, anger, emotional responses, prior beliefs, COVID-19 misinformation, intuitive thinking
MeSH terms
COVID-19, Adolescent, Adult, Affect, Anger, Austria, Communication, Emotions, Female, Humans, Deception, Male, Middle Aged, Recognition, Psychology, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review