How the Public Makes Sense of Artificial Intelligence: The Interplay Between Communication and Discrete Emotions

Abstract

While artificial intelligence (AI) garners widespread media attention as an emerging technology, empirical research on how AI-related information influences public opinion is scarce, especially among those with preexisting emotions toward AI. Utilizing a survey of 1,206 U.S. adults, this study examined how communication about AI and three discrete emotions (anger, fear, and hope) jointly affect public attitudes. More exposure to AI information from mediated sources was linked to stronger support for AI, particularly among those reporting more anger and less hope toward AI. Conversely, there was no significant association between information exposure from interpersonal sources and public support.

Keywords

artificial intelligence (AI)public opinion discrete emotions communication

Get full access to this article

View all access options for this article.

References

Alkaissi

McFarlane

S. I.

(2023). Artificial hallucinations in ChatGPT: Implications in scientific writing. Cureus, 15(2), e35179. https://doi.org/10.7759/cureus.35179

Anderson

A. A.

Brossard

Scheufele

D. A.

Xenos

M. A.

Ladwig

(2014). The “nasty effect”: Online incivility and risk perceptions of emerging technologies. Journal of Computer-Mediated Communication, 19(3), 373–387. https://doi.org/10.1111/jcc4.12009

Atkin

C. K.

Rice

R. E.

(2013). Theory and principles of public communication campaigns. In Rice

R. E.

Atkin

C. K.

(Eds.), Public communication campaigns (4th ed., pp. 3–20). Sage.

Bao

Krause

N. M.

Calice

M. N.

Scheufele

D. A.

Wirz

C. D.

Brossard

. . .Xenos

M. A.

(2022). Whose AI? How different publics think about AI and its social impacts. Computers in Human Behavior, 130, 107182. https://doi.org/10.1016/j.chb.2022.107182

Barocas

Crawford

Shapiro

Wallach

(2017, October 29). The problem with bias: From allocative to representational harms in machine learning [Conference session]. Special Interest Group for Computing, Information and Society (SIGCIS) Conference, Philadelphia, PA, United States.

Berlyne

(1970). Novelty, complexity, and hedonic value. Perception, & Psychophysics, 8(5), 279–286. https://doi.org/10.3758/BF03212593

Bingaman

Brewer

P. R.

Paintsil

Wilson

D. C.

(2021). “Siri, show me scary images of AI”: Effects of text-based frames and visuals on support for artificial intelligence. Science Communication, 43(3), 388–401. https://doi.org/10.1177/1075547021998069

Brennen

J. S.

Howard

P. N.

Nielsen

R. K.

(2018). An industry-led debate: How UK media cover artificial intelligence. Reuters Institute for the Study of Journalism. https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2018-12/Brennen_UK_Media_Coverage_of_AI_FINAL.pdf

Brewer

P. R.

Bingaman

Paintsil

Wilson

D. C.

Dawson

(2022). Media use, interpersonal communication, and attitudes toward artificial intelligence. Science Communication, 44(5), 559–592. https://doi.org/10.1177/10755470221130307

10.

Brossard

Belluck

Gould

Wirz

C. D.

(2019). Promises and perils of gene drives: Navigating the communication of complex, post-normal science. Proceedings of the National Academy of Sciences, USA, 116(16), 7692–7697. https://doi.org/10.1073/pnas.1805874115

11.

Brossard

Scheufele

D. A.

Kim

Lewenstein

B. V.

(2009). Religiosity as a perceptual filter: Examining processes of opinion formation about nanotechnology. Public Understanding of Science, 18(5), 546–558. https://doi.org/10.1177/0963662507087304

12.

Buhmann

Fieseler

(2021). Towards a deliberative framework for responsible innovation in artificial intelligence. Technology in Society, 64, 101475. https://doi.org/10.1016/j.techsoc.2020.101475

13.

Carver

C. S.

Harmon-Jones

(2009). Anger is an approach-related affect: Evidence and implications. Psychological Bulletin, 135(2), 183–204. https://doi.org/10.1037/a0013965

14.

Cave

Coughlan

Dihal

(2019). “Scary robots” examining public responses to AI. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 331–337). Association for Computing Machinery. https://doi.org/10.1145/3306618.3314232

15.

Cave

Craig

Dihal

Dillon

Montgomery

Singler

Taylor

(2018). Portrayal and perceptions of AI and why they matter. The Royal Society. https://royalsociety.org/-/media/policy/projects/ai-narratives/AI-narratives-workshop-findings.pdf

16.

Cave

Dihal

(2019). Hopes and fears for intelligent machines in fiction and reality. Nature Machine Intelligence, 1(2), 74–78. https://doi.org/10.1038/s42256-019-0020-9

17.

Chaiken

(1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(5), 752–766. https://doi.org/10.1037/0022-3514.39.5.752

18.

Choi

(2024). Temporal framing in balanced news coverage of artificial intelligence and public attitudes. Mass Communication and Society, 27(2), 384–405. https://doi.org/10.1080/15205436.2023.2248974

19.

Chuan

C. H.

Tsai

W. H. S.

Cho

S. Y.

(2019). Framing artificial intelligence in American newspapers. In Proceedings of the 2019 AAI/ACM Conference on Artificial Intelligence, Ethics, and Society (pp. 339–344). Association for Computing Machinery. https://doi.org/10.1145/3306618.3314285

20.

Cohen

West

S. G.

Aiken

L. S.

(2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Lawrence Erlbaum Associates Publishers.

21.

Conway

B. A.

Kenski

Wang

(2015). The rise of Twitter in the political campaign: Searching for intermedia agenda-setting effects in the presidential primary. Journal of Computer-Mediated Communication, 20(4), 363–380. https://doi.org/10.1111/jcc4.12124

22.

Crawford

(2021). The atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.

23.

Dignum

(2019). Responsible artificial intelligence: How to develop and use AI in a responsible way. Springer.

24.

Downs

(1972). Up and down with ecology: The issue-attention cycle. The Public Interest, 28, 38–51.

25.

Fast

Horvitz

(2017). Long-term trends in the public perception of artificial intelligence. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 31, 963–969.

26.

Fiske

S. T.

Taylor

S. E.

(2013). Social cognition: From brains to culture (2nd ed.). McGraw-Hill.

27.

Fung

T. K.

Choi

D. H.

Scheufele

D. A.

Shaw

B. R.

(2014). Public opinion about biofuels: The interplay between party identification and risk/benefit perception. Energy Policy, 73, 344–355. https://doi.org/10.1016/j.enpol.2014.05.016

28.

García-Peñalvo

Vázquez-Ingelmo

(2023). What do we mean by GenAI? A systematic mapping of the evolution, trends, and techniques involved in Generative AI. International Journal of Interactive Multimedia and Artificial Intelligence, 8, 7–16. https://doi.org/10.9781/ijimai.2023.07.006

29.

Garvey

Maskal

(2020). Sentiment analysis of the news media on artificial intelligence does not support claims of negative bias against artificial intelligence. OMICS: A Journal of Integrative Biology, 24(5), 286–299. https://doi.org/10.1089/omi.2019.0078

30.

Gibney

(2020). The battle for ethical AI at the world’s biggest machine-learning conference. Nature, 577(7792), 609. https://doi.org/10.1038/d41586-020-00160-y

31.

Gollust

S. E.

Fowler

E. F.

Nagler

R. H.

(2021). Prevalence and potential consequences of exposure to conflicting information about mammography: Results from nationally-representative survey of U.S. adults. Health Communication, 38(2), 349–362. https://doi.org/10.1080/10410236.2021.1951958

32.

Hargittai

Füchslin

Schäfer

M. S.

(2018). How do young adults engage with science and research on social media? Some preliminary findings and an agenda for future research. Social Media + Society, 4(3), 2056305118797720. https://doi.org/10.1177/2056305118797720

33.

Hart

Albarracín

Eagly

A. H.

Brechan

Lindberg

M. J.

Merrill

(2009). Feeling validated versus being correct: A meta-analysis of selective exposure to information. Psychological Bulletin, 135(4), 555–588. https://doi.org/10.1037/a0015701

34.

Hayes

A. F.

(2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Press.

35.

Hermida

(2012). Social journalism: Exploring how social media is shaping journalism. In Siapera

Veglis

(Eds.), The handbook of global online journalism (pp. 309–328). Wiley-Blackwell.

36.

S. S.

Brossard

Scheufele

D. A.

(2008). Effects of value predispositions, mass media use, and knowledge on public attitudes toward embryonic stem cell research. International Journal of Public Opinion Research, 20(2), 171–192. https://doi.org/10.1093/ijpor/edn017

37.

S. S.

Detenber

B. H.

Rosenthal

Lee

E. W.

(2014). Seeking information about climate change: Effects of media use in an extended PRISM. Science Communication, 36(3), 270–295. https://doi.org/10.1177/1075547013520238

38.

S. S.

Leow

V. J. X.

Leung

Y. W.

(2020). Driving without the brain? Effects of value predispositions, media attention, and science knowledge on public willingness to use driverless cars in Singapore. Transportation Research Part F: Traffic Psychology and Behaviour, 71, 49–61. https://doi.org/10.1016/j.trf.2020.03.019

39.

S. S.

Scheufele

D. A.

Corley

E. A.

(2010). Making sense of policy choices: Understanding the roles of value predispositions, mass media, and cognitive processing in public attitudes toward nanotechnology. Journal of Nanoparticle Research, 12(8), 2703–2715. https://doi.org/10.1007/s11051-010-0038-8

40.

S. S.

Scheufele

D. A.

Corley

E. A.

(2013). Factors influencing public risk–benefit considerations of nanotechnology: Assessing the effects of mass media, interpersonal communication, and elaborative processing. Public Understanding of Science, 22(5), 606–623. https://doi.org/10.1177/0963662511417936

41.

Hovick

S. R.

Kahlor

Liang

M. C.

(2014). Personal cancer knowledge and information seeking through PRISM: The planned risk information seeking model. Journal of Health Communication, 19(4), 511–527. https://doi.org/10.1080/10810730.2013.821556

42.

Jang

S. M.

(2014). Seeking congruency or incongruency online? Examining selective exposure to four controversial science issues. Science Communication, 36(2), 143–167. https://doi.org/10.1177/1075547013502733

43.

Jasanoff

Kim

S. H.

(2009). Containing the atom: Sociotechnical imaginaries and nuclear power in the United States and South Korea. Mineva, 47(2), 119–146. https://doi.org/10.1007/s11024-009-9124-4

44.

Jones

(2006). Against technology: From the Luddites to Neo-Luddism. Routledge.

45.

Kahlor

(2010). PRISM: A planned risk information seeking model. Health Communication, 25(4), 345–356. https://doi.org/10.1080/10410231003775172

46.

Kirkpatrick

A. W.

Hmielowski

J. D.

Boyd

(2023). Fearing the future: Examining the conditional indirect correlation of attention to artificial intelligence news on artificial intelligence attitudes. In Nah

S. D.

(Ed.), Research handbook on artificial intelligence and communication (pp. 176–192). Edward Elgar Publishing.

47.

Ladwig

Dalrymple

K. E.

Brossard

Scheufele

D. A.

Corley

E. A.

(2012). Perceived familiarity or factual knowledge? Comparing operationalizations of scientific understanding. Science and Public Policy, 39(6), 761–774. https://doi.org/10.1093/scipol/scs048

48.

Lazarus

R. S.

(1991). Emotion and adaptation. Oxford University Press.

49.

Lee

J. H.

(2008). Effects of news deviance and personal involvement on audience story selection: A web-tracking analysis. Journalism & Mass Communication Quarterly, 85(1), 41–60. https://doi.org/10.1177/107769900808500104

50.

Lenart

(1994). Shaping political attitudes: The impact of interpersonal communication and mass media. Sage.

51.

Lerner

J. S.

Gonzalez

R. M.

Small

D. A.

Fischhoff

(2003). Effects of fear and anger on perceived risks of terrorism: A national field experiment. Psychological Science, 14(2), 144–150. https://doi.org/10.1111/1467-9280.01433

52.

Lerner

J. S.

Keltner

(2000). Beyond valence: Toward a model of emotion-specific influences on judgement and choice. Cognition & Emotion, 14(4), 473–493. https://doi.org/10.1080/026999300402763

53.

Liang

S. S.

Brossard

Xenos

M. A.

Scheufele

D. A.

Anderson

A. A.

. . .He

(2015). Value predispositions as perceptual filters: Comparing of public attitudes toward nanotechnology in the United States and Singapore. Public Understanding of Science, 24(5), 582–600. https://doi.org/10.1177/0963662513510858

54.

Liu

Priest

(2009). Understanding public support for stem cell research: Media communication, interpersonal communication and trust in key actors. Public Understanding of Science, 18(6), 704–718. https://doi.org/10.1177/0963662508097625

55.

Loewenstein

G. F.

Weber

E. U.

Hsee

C. K.

Welch

E. S.

(2001). Risk as feelings. Psychological Bulletin, 127(2), 267–286. https://doi.org/10.1037/0033-2909.127.2.267

56.

Lull

R. B.

Scheufele

D. A.

(2017). Understanding and overcoming fear of the unnatural in discussion of GMOs. In Scheufele

D. A.

Kahan

D. M.

Jamieson

K. H.

(Eds.), The Oxford handbook of the science of science communication (pp. 409–420). Oxford University Press.

57.

Manikonda

Kambhampati

(2018). Tweeting AI: Perceptions of lay versus expert twitterati. Proceedings of the Twelfth International AAAI Conference on Web and Social Media, 12, 652–655.

58.

Mou

Lin

C. A.

(2014). Communicating food safety via the social media: The role of knowledge and emotions on risk perception and prevention. Science Communication, 36(5), 593–616. https://doi.org/10.1177/1075547014549480

59.

Mueller-Herbst

J. M.

Xenos

M. A.

Scheufele

D. A.

Brossard

(2020). Saw it on Facebook: The role of social media in facilitating science issue awareness. Social Media + Society, 6(2), 2056305120930412. https://doi.org/10.1177/2056305120930412

60.

Garrett

R. K.

Slater

M. D.

(2018). Rumor acceptance during public health crises: Testing the emotional congruence hypothesis. Journal of Health Communication, 23(8), 791–799. https://doi.org/10.1080/10810730.2018.1527877

61.

Nabi

R. L.

(2002). Anger, fear, uncertainty, and attitudes: A test of the cognitive-functional model. Communication Monographs, 69(3), 204–216. https://doi.org/10.1080/03637750216541

62.

Nabi

R. L.

(2010). The case for emphasizing discrete emotions in communication research. Communication Monographs, 77(2), 153–159. https://doi.org/10.1080/03637751003790444

63.

Nader

Toprac

Scott

Baker

(2024). Public understanding of artificial intelligence through entertainment media. AI & SOCIETY, 39, 713–726. https://doi.org/10.1007/s00146-022-01427-w

64.

National Science Board. (2020). Science and technology: Public attitudes, knowledge, and interest (NSB-2020-7). https://ncses.nsf.gov/pubs/nsb20207/interest-information-sources-and-involvement

65.

Nisbet

M. C.

Huge

(2006). Attention cycles and frames in the plant biotechnology debate: Managing power and participation through the press/policy connection. Harvard International Journal of Press/Politics, 11(2), 3–40. https://doi.org/10.1177/1081180X06286701

66.

Ohme

Albaek

de Vreese

C. H.

(2016). Exposure research going mobile: A smartphone-based measurement of media exposure to political information in a convergent media environment. Communication Methods and Measures, 10(2–3), 135–148. https://doi.org/10.1080/19312458.2016.1150972

67.

Oravec

J. A.

(2023). Rage against robots: Emotional and motivational dimensions of anti-robot attacks, robot sabotage, and robot bullying. Technological Forecasting and Social Change, 189, 122249. https://doi.org/10.1016/j.techfore.2022.122249

68.

Ouchchy

Coin

Dubljević

(2020). AI in the headlines: The portrayal of the ethical issues of artificial intelligence in the media. AI & SOCIETY, 35(4), 927–936. https://doi.org/10.1007/s00146-020-00965-5

69.

Pew Research Center. (2017, September 20). Science news and information today. https://www.pewresearch.org/journalism/2017/09/20/science-news-and-information-today/

70.

Philbeck

Davis

(2018). The fourth industrial revolution. Journal of International Affairs, 72(1), 17–22. https://www-jstor-org-443.web.bisu.edu.cn/stable/26588339

71.

Rose

K. M.

Howell

E. L.

L. Y. F.

Xenos

M. A.

Brossard

Scheufele

D. A.

(2019). Distinguishing scientific knowledge: The impact of different measures of knowledge on genetically modified food attitudes. Public Understanding of Science, 28(4), 449–467. https://doi.org/10.1177/0963662518824837

72.

Runge

K. K.

Brossard

Xenos

M. A.

(2018). Protective progressives to distrustful traditionalists: A post hoc segmentation method for science communication. Environmental Communication, 12(8), 1023–1045. https://doi.org/10.1080/17524032.2018.1513854

73.

Russell Neuman

Guggenheim

Mo Jang

S. A.

Bae

S. Y.

(2014). The dynamics of public attention: Agenda-setting theory meets big data. Journal of Communication, 64(2), 193–214. https://doi.org/10.1111/jcom.12088

74.

Scheufele

D. A.

(2018). Beyond the choir? The need to understand multiple publics for science. Environmental Communication, 12(8), 1123–1126. https://doi.org/10.1080/17524032.2018.1521543

75.

Scheufele

D. A.

Lewenstein

B. V.

(2005). The public and nanotechnology: How citizens make sense of emerging technologies. Journal of Nanoparticle Research, 7(6), 659–667. https://doi.org/10.1007/s11051-005-7526-2

76.

Schwab

(2016). The fourth industrial revolution. World Economic Forum.

77.

Sjöberg

(2007). Emotions and risk perception. Risk Management, 9(4), 223–237. https://doi.org/10.1057/palgrave.rm.8250038

78.

Slovic

Peters

(2006). Risk perception and affect. Current Directions in Psychological Science, 15(6), 322–325. https://doi.org/10.1111/j.1467-8721.2006.00461.x

79.

Smith

C. A.

Ellsworth

P. C.

(1985). Patterns of cognitive appraisal in emotion. Journal of Personality and Social Psychology, 48(4), 813–838.

80.

Smith

Leiserowitz

(2014). The role of emotion in global warming policy support and opposition. Risk Analysis, 34(5), 937–948. https://doi.org/10.1111/risa.12140

81.

Southwell

B. G.

Yzer

M. C.

(2007). The roles of interpersonal communication in mass media campaigns. Annals of the International Communication Association, 31(1), 420–462. https://doi.org/10.1080/23808985.2007.11679072

82.

L. Y. F.

Akin

Brossard

Scheufele

D. A.

Xenos

M. A.

(2015). Science news consumption patterns and their implications for public understanding of science. Journalism & Mass Communication Quarterly, 92(3), 597–616. https://doi.org/10.1177/1077699015586415

83.

L. Y. F.

Cacciatore

M. A.

Scheufele

D. A.

Brossard

Xenos

M. A.

(2014). Inequalities in scientific understanding: Differentiating between factual and perceived knowledge gaps. Science Communication, 36(3), 352–378. https://doi.org/10.1177/1075547014529093

84.

Tompkins

M. K.

Bjälkebring

Peters

(2018). Emotional aspects of risk perceptions. In Raue

Lerner

Streicher

(Eds.), Psychological perspectives on risk and risk analysis (pp. 109–130). Springer.

85.

United Nations Educational, Scientific and Cultural Organization. (2023). Reporting on artificial intelligence: A handbook for journalism educators. https://doi.org/10.58338/HSMK8605

86.

Valkenburg

P. M.

Peter

(2013). The differential susceptibility to media effects model. Journal of Communication, 63(2), 221–243. https://doi.org/10.1111/jcom.12024

87.

Valkenburg

P. M.

Peter

Walther

J. B.

(2016). Media effects: Theory and research. Annual Review of Psychology, 67, 315–338. https://doi.org/10.1146/annurev-psych-122414-033608

88.

Vicente

P. N.

Dias-Trindade

(2021). Reframing sociotechnical imaginaries: The case of the Fourth Industrial Revolution. Public Understanding of Science, 30(6), 708–723. https://doi.org/10.1177/09636625211013513

89.

Wilson

(2022). Public engagement and AI: A values analysis of national strategies. Government Information Quarterly, 39(1), 101652. https://doi.org/10.1016/j.giq.2021.101652

90.

Wooldridge

(2021). A brief history of artificial intelligence: What it is, where we are, and where we are going. Flatiron Books.

91.

Yang

J. Z.

Chu

(2018). Who is afraid of the Ebola outbreak? The influence of discrete emotions on risk perception. Journal of Risk Research, 21(7), 834–853. https://doi.org/10.1080/13669877.2016.1247378

92.

Yang

Krause

N. M.

Bao

Calice

M. N.

Newman

T. P.

Scheufele

D. A.

Brossard

(2023). In AI we trust: The interplay of media use, political ideology, and trust in shaping emerging AI attitudes. Journalism & Mass Communication Quarterly. Advance online publication. https://doi.org/10.1177/10776990231190868

93.

Yang

Chuah

A. S.

Lee

E. W.

S. S.

(2017). Extending the cognitive mediation model: Examining factors associated with perceived familiarity and factual knowledge of nanotechnology. Mass Communication and Society, 20(3), 403–426. https://doi.org/10.1080/15205436.2016.1271436

94.

Zhang

Dafoe

(2019, January 9). Artificial intelligence: American attitudes and trends. http://ssrn.com/abstract=3312874

95.

Zhang

Dafoe

(2020). U.S. public opinion on the governance of artificial intelligence. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 187–193). Association for Computing Machinery. https://doi.org/10.1145/3375627.3375827

96.

Zhang

Maslej

Brynjolfsson

Etchemendy

Lyons

Manyika

Ngo

Niebles

J. C.

Sellitto

Sakhaee

Shoham

Clark

Perrault

(2022). The AI index 2022 annual report. AI Index Steering Committee, Stanford Institute for Human-Centered AI, Stanford University. https://aiindex.stanford.edu/wp-content/uploads/2022/03/2022-AI-Index-Report_Master.pdf

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB