Campaign Deepfakes and Affective Polarization: The Role of Artificial Intelligence in Campaigns in Shaping Voter Attitudes

Abstract

Does exposure to news about out-party campaigns’ increasing use of deepfakes further increase polarization among partisans, and how does this impact vary among different evaluations of political coalitions, parties, and leaders? What role does approval for candidates’ usage of AI play as a moderator in this relationship? This paper examines these questions using data from a survey experiment (N = 1627), in which each respondent exposed to a news story that varied in their coverage of the growing deployment of AI-powered deepfakes during the 2024 India’s national election campaigns. The findings indicate that exposure to news about out-party campaigns’ use of deepfakes did not polarize attitudes towards coalitions, parties, and leaders. In contrast, approval for candidates’ use of AI was negatively associated with polarization towards coalitions, parties, and leaders, but this did not moderate the experimental effects: respondents who approved of candidates’ AI usage and saw a news article about out-party use of deepfakes were not more polarized toward either coalitions, parties or leaders. This study not only extends the understanding of AI’s role in campaigns by examining its complex effects on affective polarization, but it also broadens empirical research into the impact of deepfakes by focusing on the non-Western context of India, thereby offering new theoretical insights into how technological advancements intersect with political behaviors and attitudes.

Keywords

artificial intelligence deepfake political campaigns: affective polarization: partisanship

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References

Ahmed

(2021). Fooled by the fakes: Cognitive differences in perceived claim accuracy and sharing intention of non-political deepfakes. Personality and Individual Differences, 182, Article 111074. https://doi.org/10.1016/j.paid.2021.111074

Jazeera.

(2022, January 10). ‘Auction’ of India’s Muslim women shows tech weaponised for abuse | Internet News | Al Jazeera. https://www.aljazeera.com/news/2022/1/10/india-bulli-bai-app-auction-muslim-women-tech-weaponised-abuse

Appel

Prietzel

(2022). The detection of political deepfakes. Journal of Computer-Mediated Communication, 27(4), zmac008. https://doi.org/10.1093/jcmc/zmac008

Bail

C. A.

Argyle

L. P.

Brown

T. W.

Bumpus

J. P.

Chen

Hunzaker

M. B. F.

Lee

Mann

Merhout

Volfovsky

Fallin Hunzaker

M. B.

Lee

Mann

Merhout

Volfovsky

(2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216–9221. https://doi.org/10.1073/pnas.1804840115

Box-Steffensmeier

J. M.

De Boef

Lin

T. M.

(2004). The dynamics of the partisan gender gap. American Political Science Review, 98(3), 515–528. https://doi.org/10.1017/s0003055404001315

Burton

(2023). Algorithmic extremism? The securitization of artificial intelligence (AI) and its impact on radicalism, polarization and political violence. Technology in Society, 75, Article 102262. https://doi.org/10.1016/j.techsoc.2023.102262

Chesney

Citron

(2019). Deep fakes: A looming challenge for privacy, democracy, and national security. California Law Review, 107(6), 1753. https://doi.org/10.15779/Z38RV0D15J

Dan

(2025). Deepfakes as a democratic threat: Experimental evidence shows noxious effects that are reducible through journalistic fact checks. The International Journal of Press/Politics. Advance online publication. https://doi.org/10.1177/19401612251317766

Diakopoulos

Johnson

(2021). Anticipating and addressing the ethical implications of deepfakes in the context of elections. New Media & Society, 23(7), 2072–2098. https://doi.org/10.1177/1461444820925811

10.

DiMaggio

Cohen

(2005). Information inequality and network externalities: A comparative study of the diffusion of television and the internet. In Nee

Swedberg

(Eds.), The economic sociology of capitalism (pp. 227–267). Princeton University Press.

11.

Dobber

Metoui

Trilling

Helberger

de Vreese

(2021). Do (microtargeted) deepfakes have real effects on political attitudes? The International Journal of Press/Politics, 26(1), 69–91. https://doi.org/10.1177/1940161220944364

12.

Dommett

(2020). Regulating digital campaigning: The need for precision in calls for transparency. Policy & Internet, 12(4), 432–449. https://doi.org/10.1002/poi3.234

13.

Dommett

Kefford

Kruschinski

(2024). Data-Driven campaigning and political parties. Five advanced democracies compared. Oxford University Press.

14.

Druckman

J. N.

Levendusky

M. S.

(2019). What do we measure when we measure affective polarization? Public Opinion Quarterly, 83(1), 114–122. https://doi.org/10.1093/poq/nfz003

15.

Enders

A. M.

Armaly

M. T.

(2019). The differential effects of actual and perceived polarization. Political Behavior, 41(3), 815–839. https://doi.org/10.1007/s11109-018-9476-2

16.

Fang

C. S.

(2024, September 9). Bill to combat digitally manipulated content, deepfakes during elections tabled in Parliament. The Straits Times. https://www.straitstimes.com/singapore/politics/bill-to-combat-digitally-manipulated-content-deepfakes-during-elections-tabled-in-parliament

17.

Fichman

R. G.

(1992). Information technology diffusion: A review of empirical research. Proceedings of the Thirteenth International Conference on Information Systems (ICIS), Dallas, 195–206.

18.

Garmendia Madariaga

Riera

(2022). Building blocks: Online political promiscuity in times of polarization in Spain. Revista Espanola de Investigaciones Sociologicas, 178, 61–82. https://doi.org/10.5477/cis/reis.178.61

19.

Garrett

R. K.

Long

J. A.

Jeong

M. S.

(2019). From partisan media to misperception: Affective polarization as mediator. Journal of Communication, 69(5), 490–512. https://doi.org/10.1093/joc/jqz028

20.

Garzia

Ferreira da Silva

Maye

(2023). Affective polarization in comparative and longitudinal perspective. Public Opinion Quarterly, 87(1), 219–231. https://doi.org/10.1093/poq/nfad004

21.

Hernandez

Anduiza

Rico

(2021). Affective polarization and the salience of elections. Electoral Studies, 69(1), Article 102203. https://doi.org/10.1016/j.electstud.2020.102203

22.

Iyengar

Krupenkin

(2018). The strengthening of partisan affect. Political Psychology, 39(S1), 201–218. https://doi.org/10.1111/pops.12487

23.

Iyengar

Lelkes

Levendusky

Malhotra

Westwood

S. J.

(2019). The origins and consequences of affective polarization in the United States. Annual Review of Political Science, 22(1), 129–146. https://doi.org/10.1146/annurev-polisci-051117-073034

24.

Kertysova

(2018). Artificial intelligence and disinformation. Security and Human Rights, 29(1-4), 55–81. https://doi.org/10.1163/18750230-02901005

25.

Kietzmann

Lee

L. W.

McCarthy

I. P.

Kietzmann

T. C.

(2020). Deepfakes: Trick or treat? Business Horizons, 63(2), 135–146. https://doi.org/10.1016/j.bushor.2019.11.006

26.

Kirk

H. R.

Vidgen

Röttger

Hale

S. A.

(2024). The benefits, risks and bounds of personalizing the alignment of large language models to individuals. Nature Machine Intelligence, 6(4), 383–392. https://doi.org/10.1038/s42256-024-00820-y

27.

Kreiss

McGregor

S. C.

(2018). Technology firms shape political communication: The work of Microsoft, Facebook, Twitter, and Google with campaigns during the 2016 US presidential cycle. Political Communication, 35(2), 155–177.

28.

Lau

(2024). Singapore seeks to fight deepfakes in elections with new laws ahead of 2025 polls. South China Morning Post. https://www.scmp.com/week-asia/politics/article/3279357/singapore-seeks-fight-deepfakes-elections-new-laws-ahead-2025-polls

29.

Lees

Cikara

(2021). Understanding and combating misperceived polarization. Philosophical Transactions of the Royal Society B: Biological Sciences, 376(1822), Article 20200143. https://doi.org/10.1098/rstb.2020.0143

30.

Lelkes

(2021). Policy over party: Comparing the effects of candidate ideology and party on affective polarization. Political Science Research and Methods, 9(1), 189–196. https://doi.org/10.1017/psrm.2019.18

31.

Lelkes

Sood

Iyengar

(2017). The hostile audience: The effect of access to broadband internet on partisan affect. American Journal of Political Science, 61(1), 5–20. https://doi.org/10.1111/ajps.12237

32.

Lorenzo-Rodríguez

Torcal

(2023). Twitter and affective polarisation: Following political leaders in Spain. South European Society and Politics, 27(1), 97–123. https://doi.org/10.1080/13608746.2022.2047554

33.

Matz

S. C.

Kosinski

Nave

Stillwell

D. J.

(2017). Psychological targeting as an effective approach to digital mass persuasion. Proceedings of the national academy of sciences, 114(48), 12714–12719. https://doi.org/10.1073/pnas.1710966114

34.

McCoy

Somer

(2019). Toward a theory of pernicious polarization and how it harms democracies: Comparative evidence and possible remedies. The Annals of the American Academy of Political and Social Science, 681(1), 234–271. https://doi.org/10.1177/0002716218818782

35.

Mukherjee

(2023). India vs Bharat renaming row: Opposition reacts, Centre supports and netizens confused. The Mint. https://www.livemint.com/news/india/india-vs-bharat-renaming-row-opposition-reacts-centre-supports-and-netizens-confused-11693913707497.html

36.

Neyazi

T. A.

Kuru

(2022). Motivated mobilization: The role of emotions in the processing of poll messages. The International Journal of Press/Politics, 29(1), 184–205. https://doi.org/10.1177/19401612221086907

37.

Neyazi

T. A.

Kuru

Mukerjee

(2024). The influence of sexism and incivility in WhatsApp political discussions on affective polarization: Evidence from a 2022 multi-party election in India. The International Journal of Press/Politics, 30(3), 797–818. https://doi.org/10.1177/19401612241242431

38.

Neyazi

T. A.

Nadaf

A. H.

Tan

K. E.

Schroeder

(2024). Does trust in government moderate the perception towards deepfakes? Comparative perspectives from Asia on the risks of AI and misinformation for democracy. Government Information Quarterly, 41(4), Article 101980. https://doi.org/10.1016/j.giq.2024.101980

39.

Nielsen

R. K.

(2012). Ground Wars: Personalized Communication in Political Campaigns. New Jersey: Princeton University Press.

40.

Nordbrandt

(2023). Affective polarization in the digital age: Testing the direction of the relationship between social media and users’ feelings for out-group parties. New Media & Society, 25(12), 3392–3411. https://doi.org/10.1177/14614448211044393

41.

Paris

Donovan

(2019). Deepfakes and cheap fakes: The manipulation of audio and visual evidence. Data & Society Report. https://datasociety.net/library/deepfakes-and-cheap-fakes/

42.

Pawelec

(2022). Deepfakes and democracy (theory): How synthetic audio-visual media for disinformation and hate speech threaten core democratic functions. Digital Society, 1(2), 19. https://doi.org/10.1007/s44206-022-00010-6

43.

Phillips

(2022). Affective polarization: Over time, through the generations, and during the lifespan. Political Behavior, 44(3), 1483–1508. https://doi.org/10.1007/s11109-022-09784-4

44.

Rebelo

(2024). India’s generative AI election pilot shows artificial intelligence in campaigns is here to stay. AI Review, 12–16. https://mediaengagement.org/wp-content/uploads/2024/10/Indias-Generative-AI-Election-Pilot-Shows-Artificial-Intelligence-In-Campaigns-Is-Here-To-Stay.pdf

45.

Rico

Guinjoan

Anduiza

(2017). The emotional underpinnings of populism: How anger and fear affect populist attitudes. Swiss Political Science Review, 23(4), 444–461. https://doi.org/10.1111/spsr.12261

46.

Robison

Moskowitz

R. L.

(2019). The group basis of partisan affective polarization. The Journal of Politics, 81(3), 1075–1079. https://doi.org/10.1086/703069

47.

Rogers

E. M.

(2003). Diffusion of innovations (5th ed.). Free Press.

48.

Shargel

(2015). Emotions without objects. Biology & Philosophy, 30(6), 831–844. https://doi.org/10.1007/s10539-014-9473-8

49.

Stromer-Galley

(2019). Presidential campaigning in the Internet age. Oxford University Press.

50.

Susser

Roessler

Nissenbaum

(2019). Online manipulation: Hidden influences in a digital world. Georgetown Law Technology Review, 4(1), 1–45. https://doi.org/10.2139/ssrn.3306006

51.

Tajfel

(1970). Experiments in intergroup discrimination. Scientific American, 223(5), 96–103. https://doi.org/10.1038/scientificamerican1170-96

52.

Tajfel

Turner

(1979). An integrative theory of intergroup conflict. In Austin

W. G.

Worchel

(Eds.), The Social Psychology of Intergroup Relations (pp. 33–47). Monterey, CA: Brooks Cole.

53.

Törnberg

(2022). How digital media drive affective polarization through partisan sorting. Proceedings of the National Academy of Sciences, 119(42), Article e2207159119. https://doi.org/10.1073/pnas.2207159119

54.

Törnberg

Andersson

Lindgren

Banisch

(2021). Modeling the emergence of affective polarization in the social media society. PLoS One, 16(10), Article e0258259. https://doi.org/10.1371/journal.pone.0258259

55.

Vaccari

Chadwick

(2020). Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news. Social Media + Society, 6(1), Article 2056305120903408. https://doi.org/10.1177/2056305120903408

56.

Vliegenthart

Vrielink

Dommett

Gibson

Bon

Chu

Kruikemeier

(2024). Citizens’ acceptance of data-driven political campaigning: A 25-country cross-national vignette study. Social Science Computer Review, 42(5), 1101–1119. https://doi.org/10.1177/08944393241249708

57.

Williams

C. B.

Gulati

G. J. J.

(2013). Social networks in political campaigns: Facebook and the congressional elections of 2006 and 2008. New Media & Society, 15(1), 52–71. https://doi.org/10.1177/1461444812457332

58.

Zolkepli

I. A.

Kamarulzaman

(2015). Social media adoption: The role of media needs and innovation characteristics. Computers in Human Behavior, 43, 189–209. https://doi.org/10.1016/j.chb.2014.10.050

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