Personalized news recommendation systems (NRSs) have become essential tools for users to view the vast amount of online news. Current NRSs, however, face crucial challenges in ensuring users’ right to view diverse news and viewpoints. We propose a conceptual framework for personalized recommendation nudges that can promote diverse news consumption on online platforms. We test the effects of diversity nudges by examining how users make sense of algorithmic nudges and how nudges influence users’ views on personalization and attitudes toward news diversity and media pluralism. The findings show that algorithmic nudges play a crucial role in understanding normative values in NRSs, which then influence the user’s intention to consume diverse news. The results further imply the personalization paradox that personalized news recommendations can enhance and decrease user engagement with the systems. The results provide conceptual and operational bases for diversity-aware NRS design, improving the diversity and personalization of news recommendations. We offer a conceptual framework of algorithmic nudges and news diversity, and from there, we develop theoretically grounded paths for facilitating diversity and pluralism in NRSs.
BastianM.HelbergerN.WijermarsM. (2021). Safeguarding the journalistic DNA: Attitudes towards the role of professional values in algorithmic news recommender designs. Digital Journalism, 9(6), 835–863. https://doi.org/10.1080/21670811.2021.1912622
BryanovK.WatsonB.PingreeR.SantiaM. (2020). Effects of partisan personalization in a news portal experiment. Public Opinion Quarterly, 84(Suppl. 1), 216–235.
7.
BucherT. (2017). The algorithmic imaginary: Exploring the ordinary affects of Facebook algorithms. Information, Communication & Society, 20, 30–44.
8.
BuunkP.GibbonsX. (2000). Toward an enlightenment in social comparison theory. In: SulsJ.WheelerL. (Eds.), Handbook of social comparison. The Springer series in social clinical psychology (pp. 487–499). Springer. https://doi.org/10.1007/978-1-4615-4237-7_22
9.
CardenalS.Aguilar-ParedesC.CristanchoC.Majó-VázquezS. (2019). Echo-chambers in online news consumption. European Journal of Communication, 34(4), 360–376. https://doi.org/10.1177/0267323119844409
10.
CurrinC.VeraS.Khaledi-NasabA. (2022). Depolarization of echo chambers by random dynamical nudge. Scientific Reports, 12, Article 9234. https://doi.org/10.1038/s41598-022-12494-w
HeitzL.LischkaJ.BirrerA.PaudelB.TolmeijerS.LaugwitzL.BernsteinA. (2022). Benefits of diverse news recommendations for democracy. Digital Journalism, 10(10), 1710–1730. https://doi.org/10.1080/21670811.2021.2021804
HermannE. (2022). Artificial intelligence and mass personalization of communication content. New Media & Society, 24(5), 1258–1277. https://doi.org/10.1177/14614448211022702
15.
JangW.ChunJ.KimS.KangY. (2022). The effects of anthropomorphism on how people evaluate algorithm-written news. Digital Journalism, 1, 103–124. https://doi.org/10.1080/21670811.2021.1976064
KnudsenE. (2023). Modeling news recommender systems’ conditional effects on selective exposure. Journal of Communication, 73, 138–149. https://doi.org/10.1093/joc/jqac047
21.
LimJ.AhmadN.IbarahimM. (2024). Understanding user sensemaking in fairness and transparency in algorithms. AI & Society, 39, 447–490. https://doi.org/10.1007/s00146-022-01525-9
MøllerL. (2023). Designing algorithmic editors. Digital Journalism, 1–19. https://doi.org/10.1080/21670811.2023.2215832
24.
MattisN.MasurP.MöllerJ.van AtteveldtW. (2022). Nudging towards news diversity. New Media & Society. Advance online publication. https://doi.org/10.1177/14614448221104413
25.
MitovaE.BlassnigS.StrikovicE.UrmanA.HannakA.de VreeseC. H.EsserF. (2023). News recommender systems. Annals of the International Communication Association, 47, 84–113. https://doi.org/10.1080/23808985.2022.2142149
26.
MonzerC.MoellerJ.HelbergerN.EskensS. (2020). User perspectives on the news personalization process. Digital Journalism, 8(9), 1142–1162. https://doi.org/10.1080/21670811.2020.1773291
NobleS. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.
29.
RiceR. E.BucyE. P. (2023). Mapping media developments and issues: Topics, clusters, and content of JMCQ articles on communication technology/media channels, 1935–2017. Journalism & Mass Communication Quarterly, 100(4), 901–932. https://doi.org/10.1177/10776990231196895
ScheffauerR.GoyanesM.GildeZúñigaH. (2024). Social media algorithmic versus professional journalists’ news selection. Journalism, 25(4), 755–778. https://doi.org/10.1177/14648849231179804
32.
ShinD (2022). The perception of humanness in conversational journalism: An algorithmic information-processing perspective. New Media & Society, 24(12), 2680–2704. http://doi:10.1177/146144482199380
ShinD.KoerberA.LimJ. (2024). Impact of misinformation from generative AI on user information processing: How people understand misinformation on generative AI. New Media and Society. https://doi.org/10.1177/14614448241234040
35.
SonodaA.SekiY.ToriumiF. (2022). Analyzing user engagement in news application considering popularity diversity and content diversity. Journal of Computational Social Science, 5, 1595–1614. https://doi.org/10.1007/s42001-022-00179-3
36.
VermeulenJ. (2022). To nudge or not to nudge: News recommendation as a tool to achieve online media pluralism. Digital Journalism, 10(10), 1671–1690. https://doi.org/10.1080/21670811.2022.2026796
37.
Wu-OuyangB. (2024). Comparing media systems in Western democracies. Journalism & Mass Communication Quarterly, 101(1), 45–70. https://doi.org/10.1177/10776990231217482
38.
YeungK. (2017). Hyper nudge: Big data as a mode of regulation by design. Information, Communication & Society, 20(1), 118–136.
