While partisan selective exposure could drive audience fragmentation, other individual factors might also differentiate news diets. This study applies a method that disentangles the differential contributions of the individual characteristics to audience duplication networks. By analyzing a nationally representative survey about US adults’ media use in 2019 (N = 12,043), we demonstrate that news fragmentation is driven by a myriad of individual factors, such as gender, race, and religiosity. Partisanship is still an important driver. We also distinguish between media exposure and media trust, showing that many cross-cutting ties in co-exposure networks disappear when media trust is considered. We conclude that audience fragmentation research should extend beyond ideological selectivity and additionally investigate how and why other individual-level preferences differentially contribute to fragmentation both in news exposure and in news trust.
ArceneauxKJohnsonMMurphyC (2012) Polarized political communication, oppositional media hostility, and selective exposure. The Journal of Politics74(1): 174–186.
2.
BailCAArgyleLPBrownTW, et al. (2018) Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences of the United States of America115(37): 9216–9221.
3.
BakshyEMessingSAdamicLA (2015) Exposure to ideologically diverse news and opinion on Facebook. Science348(6239): 1130–1132.
4.
BarberáPJostJTNaglerJ, et al. (2015) Tweeting from left to right: is online political communication more than an echo chamber?Psychological Science26(10): 1531–1542.
5.
BaumMA (2003) Soft news and political knowledge: evidence of absence or absence of evidence?Political Communication20(2): 173–190.
6.
BaumMAGussinP (2008) In the eye of the beholder: how information shortcuts shape individual perceptions of bias in the media. Quarterly Journal of Political Science3(1): 1–31.
7.
BourdieuP (1984) Distinction: A Social Critique of the Judgment of Taste. New York; London: Routledge.
8.
BradyWJWillsJAJostJT, et al. (2017) Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences of the United States of America114(28): 7313–7318.
9.
CurranJSalovaara-MoringICoenS, et al. (2010) Crime, foreigners and hard news: a cross-national comparison of reporting and public perception. Journalism11(1): 3–19.
10.
DruckmanJNPetersonESlothuusR (2013) How elite partisan polarization affects public opinion formation. American Political Science Review107(1): 57–79.
11.
Dvir-GvirsmanSTsfatiYMenchen-TrevinoE (2016) The extent and nature of ideological selective exposure online: combining survey responses with actual web log data from the 2013 Israeli Elections. New Media & Society18(5): 857–877.
12.
EadyGNaglerJGuessA, et al. (2019) How many people live in political bubbles on social media? Evidence from linked survey and Twitter data. SAGE Open9(1): 1–21.
13.
FirthD (1995) Bias reduction of maximum likelihood estimates. Biometrika80(1): 27–38.
14.
FletcherRNielsenRK (2017) Are news audiences increasingly fragmented? A cross-national comparative analysis of cross-platform news audience fragmentation and duplication. Journal of Communication67(4): 476–498.
15.
FletcherRCorniaANielsenRK (2020) How polarized are online and offline news audiences? A comparative analysis of twelve countries. The International Journal of Press/Politics25(2): 169–195.
16.
GarethJDanielaWTrevorH, et al. (2013) An Introduction to Statistical Learning: With Applications in R. New York: Springer.
17.
GrinbergNJosephKFriedlandL, et al. (2019) Fake news on Twitter during the 2016 US presidential election. Science363(6425): 374–378.
18.
GuessACoppockA (2018) Does counter-attitudinal information cause backlash? Results from three large survey experiments. British Journal of Political Science. Epub ahead of print 5November. DOI: 10.1017/s0007123418000327.
19.
GuoL (2012) The application of social network analysis in agenda setting research: a methodological exploration. Journal of Broadcasting & Electronic Media56(4): 616–631.
20.
GuoLVargoC (2020) “Fake news” and emerging online media ecosystem: an integrated intermedia agenda-setting analysis of the 2016 US presidential election. Communication Research47(2): 178–200.
21.
HasebrinkUPoppJ (2006) Media repertoires as a result of selective media use. A conceptual approach to the analysis of patterns of exposure. Communications31(3): 369–387.
22.
IyengarSHahnKS (2009) Red media, blue media: evidence of ideological selectivity in media use. Journal of Communication59(1): 19–39.
23.
JonesDA (2004) Why Americans don’t trust the media: a preliminary analysis. Harvard International Journal of Press/Politics9(2): 60–75.
24.
JostJTFedericoCMNapierJL (2009) Political ideology: its structure, functions, and elective affinities. Annual Review of Psychology60: 307–337.
Knobloch-WesterwickSAlterS (2007) The gender news use divide: Americans’ sex-typed selective exposure to online news topics. Journal of Communication57(4): 739–758.
27.
KnudsenEJohannessonMP (2019) Beyond the limits of survey experiments: how conjoint designs advance causal inference in political communication research. Political Communication36(2): 259–271.
28.
LaddJM (2012) Why Americans Hate the News Media and How It Matters. Princeton, NJ: Princeton University Press.
29.
LeeAMChyiHI (2014) When newsworthy is not noteworthy: examining the value of news from the audience’s perspective. Journalism Studies15(6): 807–820.
30.
LeeFLZhangY (2019) A network analytic approach to selective consumption of newspapers: the impact of politics, market, and technological platform. Journalism & Mass Communication Quarterly. Epub ahead of print 31July. DOI: 10.1177/1077699019858988.
31.
LeeHYangJ (2014) Political knowledge gaps among news consumers with different news media repertoires across multiple platforms. International Journal of Communication8: 597–617.
32.
LeeTT (2005) The liberal media myth revisited: an examination of factors influencing perceptions of media bias. Journal of Broadcasting & Electronic Media49(1): 43–64.
33.
Lehman-WilzigSNSeletzkyM (2010) Hard news, soft news, “general” news: the necessity and utility of an intermediate classification. Journalism11(1): 37–56.
34.
LindellJ (2018) Distinction recapped: digital news repertoires in the class structure. New Media & Society20: 83029–83049.
35.
LukeDA (2015) A User’s Guide to Network Analysis in R. London: Springer.
MitchelsteinEBoczkowskiPJ (2010) Online news consumption research: an assessment of past work and an agenda for the future. New Media & Society12(7): 1085–1102.
38.
MukerjeeSYangT (2020) Choosing to Avoid? A conjoint experimental study to understand selective exposure and avoidance on social media. Political Communication, 1–19.
39.
MukerjeeSMajó-VázquezSGonzález-BailónS (2018) Networks of audience overlap in the consumption of digital news. Journal of Communication68(1): 26–50.
40.
NewmanNFletcherRKalogeropoulosA, et al. (2019) Reuters Institute Digital News Report 2019. Oxford: Reuters Institute for the Study of Journalism.
41.
PengY (2018) Same candidates, different faces: uncovering media bias in visual portrayals of presidential candidates with computer vision. Journal of Communication68(5): 920–941.
42.
PennycookGRandDG (2019) Fighting misinformation on social media using crowdsourced judgments of news source quality. Proceedings of the National Academy of Sciences of the United States of America116(7): 2521–2526.
43.
PriorM (2003) Any good news in soft news? The impact of soft news preference on political knowledge. Political Communication20(2): 149–171.
44.
PriorM (2005) News vs. entertainment: how increasing media choice widens gaps in political knowledge and turnout. American Journal of Political Science49(3): 577–592.
45.
PriorM (2013) Media and political polarization. Annual Review of Political Science16: 101–127.
46.
ReinemannCStanyerJScherrS, et al. (2011) Hard and soft news: a review of concepts, operationalizations and key findings. Journalism13(2): 221–239.
47.
ReisJCKwakHAnJ, et al. (2017) Demographics of news sharing in the US Twittersphere. In: Proceedings of the 28th ACM conference on hypertext and social media, Prague, Czech Republic, 4–7 July, pp. 195–204. New York: ACM.
48.
RilesJMPilnyATewksburyD (2018) Media fragmentation in the context of bounded social networks: how far can it go?New Media & Society20(4): 1415–1432.
49.
ShehataAStrömbäckJ (2011) A matter of context: a comparative study of media environments and news consumption gaps in Europe. Political Communication28(1): 110–134.
50.
StrömbäckJ (2005) In search of a standard: four models of democracy and their normative implications for journalism. Journalism Studies6(3): 331–345.
51.
StroudNJ (2008) Media use and political predispositions: revisiting the concept of selective exposure. Political Behavior30(3): 341–366.
52.
SunsteinCR (2017) #Republic: Divided Democracy in the Age of Social Media. Princeton, NJ: Princeton University Press.
53.
TanejaHWebsterJG (2016) How do global audiences take shape? The role of institutions and culture in patterns of web use. Journal of Communication66(1): 161–182.
54.
TanejaHWebsterJGMalthouseEC, et al. (2012) Media consumption across platforms: identifying user-defined repertoires. New Media & Society14(6): 951–968.
55.
TewksburyD (2005) The seeds of audience fragmentation: specialization in the use of online news sites. Journal of Broadcasting & Electronic Media49(3): 332–348.
56.
TewksburyDRittenbergJ (2012) News on the Internet: Information and Citizenship in the 21st Century. Oxford: Oxford University Press.
57.
TsfatiY (2003) Does audience skepticism of the media matter in agenda setting?Journal of Broadcasting & Electronic Media47(2): 157–176.
58.
TsfatiYCappellaJN (2005) Why do people watch news they do not trust? The need for cognition as a moderator in the association between news media skepticism and exposure. Media Psychology7(3): 251–271.
59.
TurnerJ (2007) The messenger overwhelming the message: ideological cues and perceptions of bias in television news. Political Behavior29(4): 441–464.
60.
Van ReesKVan EijckK (2003) Media repertoires of selective audiences: the impact of status, gender, and age on media use. Poetics31(5–6): 465–490.
61.
Vara-MiguelA (2020) Cross-national similarities and differences between legacy and digital-born news media audiences. Media and Communication8(2): 16–27.
62.
VermaNFleischmannKRKoltaiKS (2018) Demographic factors and trust in different news sources. Proceedings of the Association for Information Science and Technology55(1): 524–533.
63.
ViswanathKFinneganJR (1996) The knowledge gap hypothesis: twenty-five years later. Annals of the International Communication Association19(1): 187–228.
64.
VittinghoffEGliddenDVShiboskiSC, et al. (2011) Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. New York: Springer.
65.
WallaceLEWegenerDTPettyRE (2019) Influences of source bias that differ from source untrustworthiness: when flip-flopping is more and less surprising. Journal of Personality and Social Psychology118(4): 603–616.
66.
WebsterJG (2005) Beneath the veneer of fragmentation: television audience polarization in a multichannel world. Journal of Communication55(2): 366–382.
67.
WebsterJG (2011) The duality of media: a structurational theory of public attention. Communication Theory21(1): 43–66.
68.
WebsterJGKsiazekTB (2012) The dynamics of audience fragmentation: public attention in an age of digital media. Journal of Communication62(1): 39–56.
69.
YangTMajó-VázquezSNielsenRK, et al. (2020) Exposure to news grows less fragmented with an increase in mobile access. Proceedings of the National Academy of Sciences117(46): 28678–28683.
70.
YoungIM (2002) Inclusion and Democracy. New York: Oxford University Press.
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.
