The degree of heterogeneity of news consumption in Germany—Descriptive statistics and relations with individual differences in personality,ideological attitudes,and voting intentions
Restricted accessResearch articleFirst published online February, 2024
The degree of heterogeneity of news consumption in Germany—Descriptive statistics and relations with individual differences in personality,ideological attitudes,and voting intentions
This study aimed to examine the degree of homogeneity versus heterogeneity of individuals’ political information environments across offline and online media types and relations with sociodemographic variables, personality, and political attitudes. In two online surveys, German participants (sample 1: N = 686; sample 2: N = 702) provided information on sociodemographic variables, consumption of political news, and voting intentions, and completed the Big Five Inventory and Right-Wing Authoritarianism (RWA) and Social Dominance Orientation (SDO) scales. Results revealed that absolutely homogeneous political news consumption was evident for a small proportion of individuals (2.04% and 0.43%). Openness (positively) and Agreeableness (negatively) exhibited significant associations with the degree of heterogeneity of political information environments across samples. No consistent patterns of relations with either the ideological attitudes of RWA and SDO or voting intentions were observed. The findings shed light on the existence of absolutely homogeneous political information environments and “who” might be prone to a more homogeneous versus more heterogeneous information environment.
AnJQuerciaDCrowcroftJ (2013) Fragmented social media: a look into selective exposure to political news. In: WWW’13 Companion: Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil, 13-17 May 2013, pp. 51–52. New York: ACM Press. DOI: 10.1145/2487788.2487807
2.
BailCAArgyleLPBrownTW, et al. (2018) Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences115(37): 9216–9221.
3.
BakshyEMessingSAdamicLA (2015) Exposure to ideologically diverse news and opinion on Facebook. Science348(6239): 1130–1132.
4.
BarberáP (2015) How social media reduces mass political polarization. Evidence from Germany, Spain, and the U.S. In: Paper Prepared for the 2015 APSA Conference, Canberra. Available at: http://pablobarbera.com/static/barbera_polarization_APSA.pdf (accessed 8 March 2021)
5.
BeierleinCAsbrockFKauffM, et al. (2014) Die Kurzskala Autoritarismus (KSA-3): Ein ökonomisches Messinstrument zur Erfassung dreier Subdimensionen autoritärer Einstellungen (GESIS-Working Papers 2014-35). Mannheim: GESIS—Leibniz-Institut für Sozialwissenschaften.
6.
BeneschC (2012) An empirical analysis of the gender gap in news consumption. Journal of Media Economics25(3): 147–167.
7.
BerggrenMAkramiNBerghR, et al. (2019) Motivated social cognition and authoritarianism. Journal of Individual Differences40(4): 204–212.
8.
BessiA (2016) Personality traits and echo chambers on Facebook. Computers in Human Behavior65: 319–324.
9.
BozdagEvan den HovenJ (2015) Breaking the filter bubble: democracy and design. Ethics and Information Technology17(4): 249–265.
ButlerJC (2000) Personality and emotional correlates of right-wing authoritarianism. Social Behavior and Personality: An International Journal28(1): 1–14.
12.
CohenJ (1992) A power primer. Psychological Bulletin112(1): 155–159.
13.
CostaPTMcCraeRR (1992a) Four ways five factors are basic. Personality and Individual Differences13(6): 653–665.
14.
CostaPTMcCraeRR (1992b) Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) Professional Manual. Odessa, Fl: Psychological Assessment Resources.
15.
DillonW. R.WestinS. (1982). Scoring frequency data for discriminant analysis: Perhaps discrete procedures can be avoided. Journal of Marketing Research (JMR)19(1), 44–56. https://doi.org/10.2307/3151529
16.
DuckittJSibleyCG (2009) A dual-process motivational model of ideology, politics, and prejudice. Psychological Inquiry20(2–3): 98–109.
17.
DuckittJSibleyCG (2010) Personality, ideology, prejudice, and politics: a dual-process motivational model. Journal of Personality78(6): 1861–1894.
18.
EdmondsGWJacksonJJFayardJV, et al. (2008) Is character fate, or is there hope to change my personality yet?Social and Personality Psychology Compass2(1): 399–413.
19.
FiskeDW (1949) Consistency of the factorial structures of personality ratings from different sources. The Journal of Abnormal and Social Psychology44(3): 329–344.
20.
FlaxmanSGoelSRaoJM (2016) Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly80(S1): 298–320.
GentzkowMShapiroJM (2011) Ideological segregation online and offline. The Quarterly Journal of Economics126(4): 1799–1839.
23.
GerberASHuberGADohertyD, et al. (2012) Disagreement and the avoidance of political discussion: aggregate relationships and differences across personality traits. American Journal of Political Science56(4): 849–874.
GoldbergLR (1990) An alternative “description of personality”: the big-five factor structure. Journal of Personality and Social Psychology59(6): 1216–1229.
26.
GötzFMGoslingSDRentfrowPJ (2021) Small effects: the indispensable foundation for a cumulative psychological science. Perspectives on Psychological Science. Epub ahead of print 2 July 2021. DOI: 10.1177/1745691620984483
27.
HeatherlyKALuYLeeJK (2017) Filtering out the other side? Cross-cutting and like-minded discussions on social networking sites. New Media & Society19(8): 1271–1289.
28.
HibbingMVRitchieMAndersonMR (2011) Personality and political discussion. Political Behavior33(4): 601–624.
29.
JohnOPSrivastavaS (1999) The big five trait taxonomy: history, measurement, and theoretical perspectives. In: PervinLJohnOP (eds) Handbook of Personality: Theory and Research. 2nd ed.New York: Guilford Press, pp. 102–138.
30.
JohnOPDonahueEMKentleRL (1991) The Big-Five Inventory-Version 4A and 54. Berkley, CA: Berkeley Institute of Personality and Social Research, University of California.
31.
KannenC (2018) ckannen Softwareentwicklung. Available at: https://ckannen.com/ (accessed 28 September 2020)
KimMKimC (2018) Personality basis for partisan news media use: openness to experience and consumption of liberal news media. Mass Communication and Society21(6): 814–833.
34.
LeeJKChoiJKimC, et al. (2014) Social media, network heterogeneity, and opinion polarization. Journal of Communication64(4): 702–722.
35.
LevyR (2021) Social media, news consumption, and polarization: evidence from a field experiment. American Economic Review111(3): 831–870.
36.
McDonaldJD (2008) Measuring personality constructs: the advantages and disadvantages of self-reports, informant reports and behavioural assessments. Enquire1(1): 75–94.
37.
MatzSC (2021) Personal echo chambers: openness-to-experience is linked to higher levels of psychological interest diversity in large-scale behavioral data. Journal of Personality and Social Psychology. Epub ahead of print 7 January 2021. DOI: 10.1037/pspp0000324.
MilesJShevlinM (2001) Applying Regression and Correlation: A Guide for Students and Researchers. London: SAGE.
40.
MinSJWohnDY (2020) Underneath the filter bubble: the role of weak ties and network cultural diversity in cross-cutting exposure to disagreements on social media. The Journal of Social Media in Society9(1): 22–38.
41.
MondakJJHalperinKD (2008) A framework for the study of personality and political behaviour. British Journal of Political Science38(2): 335–362.
42.
MortalM (2011) Vorauseilender Gehorsam Durch Die „Perfekte Frage“—Die Bestimmung Eines Schwellenwertes Von Vorauseilendem Gehorsam. Wien: Universität Wien. Available at: https://othes.univie.ac.at/13591/1/2011-03-09_0206162.pdf (accessed 3 December 2020)
NoorP (2017) The fact that we have access to so many different opinions is driving us to believe that we’re in information bubbles. The Psychologist30: 44–47.
45.
Open Science Collaboration (2015) Estimating the reproducibility of psychological science. Science349(6251): aac4716. DOI: 10.1126/science.aac4716
46.
PariserE (2011) The Filter Bubble: What the Internet Is Hiding from You. London: Penguin UK.
47.
PrattoFSidaniusJStallworthLM, et al. (1994) Social dominance orientation: a personality variable predicting social and political attitudes. Journal of Personality and Social Psychology67(4): 741–763.
48.
R Core Team (2018) R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. Available at: https://www.R-project.org/
49.
RammstedtBDannerD (2017) Die Facettenstruktur des Big Five Inventory (BFI). Diagnostica63(1): 70–84.
50.
RobertsBWMroczekD (2008) Personality trait change in adulthood. Current Directions in Psychological Science17(1): 31–35.
SindermannCElhaiJDMoshagenM, et al. (2020) Age, gender, personality, ideological attitudes and individual differences in a person’s news spectrum: how many and who might be prone to “filter bubbles” and “echo chambers” online?Heliyon6(1): e03214.
55.
SindermannCSchmittHRozgonjukD, et al. (2021) The evaluation of fake and true news: on the role of intelligence, personality, interpersonal trust, ideological attitudes, and news consumption. Heliyon7(3): e06503.
56.
SunsteinCR (2004) Democracy & filtering. Communication to the ACM47(12): 57–59.
57.
SunsteinCR (2006) Infotopia: How Many Minds Produce Knowledge. Oxford: Oxford University Press.
58.
SunsteinCR (2007) Republic.com 2.0. Princeton, NJ: Princeton University Press.
TukeyJW (1977) Exploratory Data Analysis. 1st ed. Reading, MA: Pearson.
61.
TupesECChristalRE (1992) Recurrent personality factors based on trait ratings. Journal of Personality60(2): 225–251.
62.
VaccariCValerianiABarberáP, et al. (2016) Of echo chambers and contrarian clubs: exposure to political disagreement among German and Italian users of Twitter. Social Media + Society2(3): 2056305116664221.
63.
VolkensABurstTKrauseW, et al. (2020) The Manifesto Data Collection. Manifesto Project (MRG/CMP/MARPOR). Version 2020b.—MARPOR Data Dashboard :: Single Country. Berlin: Wissenschaftszentrum Berlin Für Sozialforschung. DOI: 10.25522/manifesto.mpds.2020b
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