Skip to main content

Polarized Networks? New Evidence on American Voters’ Political Discussion Networks

Abstract

An important mechanism of mass political polarization involves citizens’ social networks: how politically homogeneous are they, how has this changed over recent years, and which individual and contextual variables predict the degree of homogeneity in social networks? Moreover, what are the consequences of network homophily on political preferences and in and out-group perceptions? In this paper, we address these questions by combining data from the 2000 American National Election Study and original data from the 2016 Cooperative Congressional Election Study. Both surveys ask respondents a battery of questions about the individuals with whom they most frequently discuss politics, including perceived vote choice and level of political knowledge. Using these data, we offer an updated empirical assessment of how polarization is influencing—and is influenced by—social network homophily.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Notes

  1. First developed by Laumann (1973), then applied in political science by Huckfeldt and Sprague (1995), these question batteries use a compound name generator which is meant to cover the nature of the interactions between respondents and their discussants. Included in political name generators are each respondent’s relationship with the discussant and the discussant’s perceived political partisanship. The political name generator used in this study was included in the ANES Time Series Study in 2000 and the 2016 CCES. Similar, but not exactly matching, batteries have been implemented in a variety of other studies.

  2. Environmental context refers to the structurally imposed areas in which an individual resides. As we explain below, we focus on and individual’s county as their environmental context in our analysis.

  3. Recent work on residential sorting has pointed out that economic factors, proximity to work, and neighborhood quality limit opportunities for individuals to engage in partisan residential sorting (Tam Cho et al. 2013; Mummolo and Nall 2017).

  4. Though the network questions in the 2016 CCES were designed to be identical to the those in the 2000 battery, the surveys themselves differ in some important ways. The 2000 ANES is a nationally representative survey administered face-to-face and over telephone, while the 2016 CCES is an opt-in survey administered over the internet. Samples from opt-in surveys tend to overrepresent politically interested and engaged voters (see Malhotra and Krosnick 2007; though we show in an appendix that the increase in network homophily from 2000 to 2016 persists when we compare low and high political sophistication groups separately.

  5. A “compound” name generator approach uses only one name generator for the same respondents. These standard political name generators gather information on interactions, a particular topic, and attributes of discussion partners. A “multiplex” name generator approach uses more than one name generator for the same respondents. See Sokhey and Djupe (2014) for a more detailed discussion.

  6. Studies examining the implications of online versus face-to-face data collection of name generator data have been inconsistent. Online surveys avoid issues related to interviewer effects present in face-to-face interviews, including social pressure which may artificially inflate the number of discussants reported. At the same time, online surveys could limit the number of discussants reported through survey design. However, both modes of data collection generally yield name generator data of comparable quality (see Brashears 2011 and Eveland et al. 2018 for additional discussion).

  7. Invalid responses to the name generator, including “NA” and “none of your business” were excluded from the analysis. Respondents who did not support a candidate are coded as not voting in the election. Discussants who the respondent did not think had voted or voted for a minor candidate are coded as disagreeing with the respondent. Discussants who the respondents could not report preference for were not included in the analysis.

  8. As a robustness check, we also estimated network homophily after switching the third and fourth discussants in the 2000 ANES for the 327 respondents who named a fourth discussant. The results, provided in the appendix, are virtually identical to those presented below.

  9. The mean number of discussants in 2000 was 1.60 while the mean in 2016 was 1.95. Individuals who do not provide discussant names are assumed to have no discussants. In our analyses, we include only respondents who responded to the name generator.

  10. See the appendix for question wording. In the case of the 2016 CCES, interviewers were not used. Instead, respondents were prompted online to answer a short series of questions about each discussant.

  11. Replication materials for this manuscript are available at https://doi.org/10.7910/DVN/3VQXIM.

  12. There are a variety of alternatives to measuring political disagreement and homogeneity. See Klofstad et al. (2013) and Lupton and Thornton (2017) for reviews.

  13. Huckfeldt et al. (2004) provide additional details about disagreement in American voters’ political communication networks in 2000.

  14. It is important to note that some research, including Eveland et al. (2013), shows that people experience higher levels of disagreement in their larger (and more peripheral) networks. This disagreement does not get picked up by name generators, which capture core discussion networks.

  15. As noted by Huckfeldt and Sprague (1995, p. 1030), “Citizens do not reside in a single environment of public opinion, but rather in a series of nested, cascading, overlapping environments that are both larger and smaller than the county unit. A real challenge of political analysis is to understand individual citizens within this variety of settings, and hence our analysis of counties is not intended to preclude analyses at other levels.” This analysis is meant to be interpreted in the same manner.

  16. See the appendix for full model specification and complete list of controls. Party identification and ideological self-placement are coded so that higher values indicate higher levels of Republican/conservative identification.

  17. The results in Tables 1 and 2 are unweighted. Weighted versions of the results are provided in the appendix, and are substantively similar to those obtained without the use of survey weights.

  18. Other research suggests the same is true of online discussion networks (Brundidge 2010).

  19. The results in this section replicate using data from the 2008-09 ANES Panel Study. Using these data, we see overall patterns which more closely resemble those seen in during the 2000 presidential election. See the appendix for important notes about how data from that Panel Study differs from the surveys used analysis presented here.

  20. Respondents to the 2000 ANES were asked to place themselves and the Democratic and Republican parties, President Clinton, Al Gore, and George Bush on the liberal-conservative and other issue scales. Respondents to the 2016 CCES were asked to place themselves alongside the Democratic and Republican parties, Hillary Clinton, Donald Trump, President Obama, Merrick Garland, and the Supreme Court on the liberal-conservative scale. CCES respondents were also asked about their governor, Senators/Senate candidates, and Representative/Representative candidates, but we exclude these from the analysis since they are not common across respondents.

  21. The scaling procedure uses respondents’ placements of all stimuli to estimate the distortion parameters. Partisans who place out-party stimuli at extreme ideological positions also tend to overstate how mainstream their own views are (i.e., the “false consensus” effect), placing themselves and in-party stimuli at more centrist positions on the liberal-conservative scale (Ross et al. 1977; Westfall et al. 2015; Hare et al. 2015, p. 765-766). Both sources of bias contribute to the magnitude of the \(\alpha _{i}\) (or “shift”) parameter.

  22. BAM allows for heteroskedastic error by estimating both individual and stimuli-specific error terms, hence the indexing on \(u_{ij}\).

  23. Visual inspection of the chains and use of the Geweke and Gelman-Rubin diagnostics indicate successful convergence on the posterior target distributions.

  24. In both surveys, the median score is 0.67 (i.e., correctly answering two of the three knowledge items) and the modal score is 1 (i.e., correctly answering all three items). Hence, we code respondents with scores between 0 and 0.67 as low sophistication and scores of 1 as high sophistication to create groups that are as equally sized as possible.

  25. Additional details provided in the appendix.

  26. Of course, even an increase in reported network homophily is consequential, as it suggests partisans have become more likely to (1) view regular political conversations and perhaps general relationships with outparty members as socially undesirable and/or (2) ignore cross-cutting political messages from outparty discussants, eliminating their influence.

References

  • Ahler, D. J., & Sood, G. (2018). The parties in our heads: Misperceptions about party composition and their consequences. Journal of Politics, 80(3), 964–981.

    Article  Google Scholar 

  • Aldrich, J. H., & McKelvey, R. D. (1977). A method of scaling with applications to the 1968 and 1972 presidential elections. American Political Science Review, 71(1), 111–130.

    Article  Google Scholar 

  • Allport, G. W. (1954). The Nature of Prejudice. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Arceneaux, K., Johnson, M., & Cryderman, J. (2013). Communication, persuasion, and the conditioning value of selective exposure: Like minds may unite and divide but they mostly tune out. Political Communication, 30(2), 213–231.

    Article  Google Scholar 

  • Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to right: Is online political communication more than an echo chamber? Psychological Science, 26(10), 1531–1542.

    Article  Google Scholar 

  • Berelson, B. R., Lazarsfeld, P. F., & McPhee, W. N. (1954). Voting: A study of opinion formation in a presidential election. Chicago, IL: Unuversity of Chicago Press.

    Google Scholar 

  • Bishop, B. (2008). The big sort: Why the clustering of like-minded America is tearing us apart. New York: Houghton Mifflin Harcourt.

    Google Scholar 

  • Bonica, A. (2014). Mapping the ideological marketplace. American Journal of Political Science, 58(2), 367–386.

    Article  Google Scholar 

  • Brady, H. E., & Sniderman, P. M. (1985). Attitude attribution: A group basis for political reasoning. American Political Science Review, 79(4), 1061–1078.

    Article  Google Scholar 

  • Brashears, M. E. (2011). Small networks and high isolation? A reexamination of American discussion networks. Social Networks, 33(4), 331–341.

    Article  Google Scholar 

  • Brundidge, J. (2010). Encountering “Difference” in the contemporary public sphere: The contribution of the internet to the heterogeneity of political discussion networks. Journal of Communication, 60, 680–700.

    Article  Google Scholar 

  • Buttice, M. K., Huckfeldt, R., & Ryan, J. B. (2009). Polarization, attribution, and communication networks in the 2006 congressional elections. In J. J. Mondak & D. G. Mitchell (Eds.), Fault Lines (pp. 42–60). New York: Routledge.

    Google Scholar 

  • Crisp, R. J., & Turner, R. N. (2009). Can imagined interactions produce positive perceptions? Reducing prejudice through simulated social contact. American Psychologist, 64(4), 231–240.

    Article  Google Scholar 

  • Druckman, J. N., Levendusky, M. S., & McLain, A. (2018). No need to watch: How the effects of partisan media can spread via interpersonal discussions. American Journal of Political Science, 62(1), 99–112.

    Article  Google Scholar 

  • Dyck, J. J., & Pearson-Merkowitz, S. (2014). To know you is not necessarily to love you: The partisan mediators of intergroup contact. Political Behavior, 36(3), 553–580.

    Article  Google Scholar 

  • Enders, A. M., & Armaly, M. T. (2019). The differential effects of actual and perceived polarization. Political Behavior, 41(3), 815–839.

    Article  Google Scholar 

  • Eveland, J., William, P., Hutchens, M. J., & Morey, A. C. (2013). Political network size and its antecedents and consequences. Political Communication, 30(3), 371–394.

    Article  Google Scholar 

  • Eveland, J., William, P., Appiah, O., & Beck, P. A. (2018). Americans are more exposed to difference than we think: Capturing hidden exposure to political and racial difference. Social Networks, 52, 192–200.

    Article  Google Scholar 

  • Finifter, A. W. (1974). The friendship group as a protective environment for political deviants. American Political Science Review, 68(2), 607–625.

    Article  Google Scholar 

  • Gaines, N. S., & Garand, J. C. (2010). Morality, equality, or locality: Analyzing the determinants of support for same-sex marriage. Political Research Quarterly, 63(3), 553–567.

    Article  Google Scholar 

  • Graham, J., Nosek, B. A., & Haidt, J. (2012). The moral stereotypes of liberals and conservatives: Exaggeration of differences across the political spectrum. PloS ONE, 7(12), e50092.

    Article  Google Scholar 

  • Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91(3), 481–510.

    Article  Google Scholar 

  • Hare, C., Armstrong, D. A., Bakker, R., Carroll, R., & Poole, K. T. (2015). Using Bayesian Aldrich-McKelvey scaling to study citizens’ ideological preferences and perceptions. American Journal of Political Science, 59(3), 759–774.

    Article  Google Scholar 

  • Hayes, R. B. (1989). The day-to-day functioning of close versus casual friendships. Journal of Social and Personal Relationships, 6, 21–37.

    Article  Google Scholar 

  • Henry, P. J., & Napier, J. L. (2017). Education is related to greater ideological prejudice. Public Opinion Quarterly, 81(4), 930–942.

    Article  Google Scholar 

  • Huber, G. A., & Malhotra, N. (2017). Political homophily in social relationships: Evidence from online dating behavior. Journal of Politics, 79(1), 269–283.

    Article  Google Scholar 

  • Huckfeldt, R. (1983). Social contexts, social networks, and urban neighborhoods: Environmental constraints on friendship choice. American Journal of Sociology, 89(3), 651–669.

    Article  Google Scholar 

  • Huckfeldt, R. (2017). Interdependence, communication, and aggregation: Transforming voters into electorates. PS: Political Science & Politics, 50(1), 3–11.

    Google Scholar 

  • Huckfeldt, R., Mendez, J. M., & Osborn, T. (2004). Disagreement, ambivalence, and engagement: The political consequences of heterogeneous networks. Political Psychology, 25(1), 65–95.

    Article  Google Scholar 

  • Huckfeldt, R., & Sprague, J. (1987). Networks in context: The social flow of political information. American Political Science Review, 81(4), 1197–1216.

    Article  Google Scholar 

  • Huckfeldt, R., & Sprague, J. (1995). Citizens, politics, and social communication: Information and influence in an election campaign. New York: Cambridge University Press.

    Book  Google Scholar 

  • Huckfeldt, R., Sprague, J., & Levine, J. (2000). The dynamics of collective deliberation in the 1996 election: Campaign effects on accessibility, certainty, and accuracy. American Political Science Review, 94(3), 641–651.

    Article  Google Scholar 

  • Huckfeldt, R., Beck, P. A., Dalton, R. J., & Levine, J. (1995). Political environments, cohesive social groups, and the communication of public opinion. American Journal of Political Science, 39(4), 1025–1054.

    Article  Google Scholar 

  • Huckfeldt, R., Johnson, P. E., & Sprague, J. (2004). Political disagreement: The survival of diverse opinions within communication networks. New York: Cambridge University Press.

    Book  Google Scholar 

  • Iyengar, S., Sood, G., & Lelkes, Y. (2012). Fear and loathing in party politics: A social identity perspective on polarization. Public Opinion Quarterly, 76(3), 405–431.

    Article  Google Scholar 

  • Iyengar, S., & Westwood, S. J. (2015). Fear and loathing across party lines: New evidence on group polarization. American Journal of Political Science, 59(3), 690–707.

    Article  Google Scholar 

  • Iyengar, S., Konitzer, T., & Tedin, K. (2018). The home as a political fortress: Family agreement in an era of polarization. Journal of Politics, 80(4), 1326–1338.

    Article  Google Scholar 

  • Key, V. O, Jr. (1949). Southern politics in state and nation. New York: Alfred A. Knopf.

    Google Scholar 

  • King, G., Murray, C. J. L., Salomon, J. A., & Tandon, A. (2004). Enhancing the validity and cross-cultural comparability of measurement in survey research. American Political Science Review, 98(1), 191–207.

    Article  Google Scholar 

  • Klar, S. (2014). Partisanship in a social setting. American Journal of Political Science, 58(3), 687–704.

    Article  Google Scholar 

  • Klar, S., Krupnikov, Y., & Ryan, J. B. (2018). Affective polarization of partisan disdain? Untangling a dislike for the opposing party from a dislike of partisanship. Public Opinion Quarterly, 82(2), 379–390.

    Article  Google Scholar 

  • Klar, S., & Shmargad, Y. (2017). The effect of network structure on preference formation. Journal of Politics, 79(2), 717–721.

    Article  Google Scholar 

  • Klofstad, C. A., Sokhey, A. E., & McClurg, S. D. (2013). Disagreeing about disagreement: How conflict in social networks affects political behavior. American Journal of Political Science, 57(1), 120–134.

    Article  Google Scholar 

  • Lang, C., & Pearson-Merkowitz, S. (2015). Partisan sorting in the United States, 1972–2012: New evidence from a dynamic analysis. Political Geography, 48, 119–129.

    Article  Google Scholar 

  • Laumann, E. (1973). Bonds of pluralism: The form and substance of urban social networks. New York: Wiley Interscience.

    Google Scholar 

  • Lazer, D., Rubineau, B., Chetkovich, C., Katz, N., & Neblo, M. (2010). The coevolution of networks and political attitudes. Political Communication, 27(3), 248–274.

    Article  Google Scholar 

  • Lelkes, Y. (2018). Affective polarization and ideological sorting: A reciprocal, albeit weak, relationship. The Forum, 16(1), 67–79.

    Article  Google Scholar 

  • Lelkes, Y., Sood, G., & Iyengar, S. (2017). The hostile audience: The effect of access to broadband internet on partisan affect. American Journal of Political Science, 61(1), 5–20.

    Article  Google Scholar 

  • Levendusky, M. S., & Malhotra, N. (2015). (Mis)perceptions of partisan polarization in the American public. Public Opinion Quarterly, 80(S1), 378–391.

    Article  Google Scholar 

  • Levitan, L. C., & Verhulst, B. (2016). Conformity in groups: The effects of others’ views on expressed attitudes and attitude change. Political Behavior, 38(2), 277–315.

    Article  Google Scholar 

  • Lupton, R., & Thornton, J. (2017). Disagreement, diversity, and participation: Examining the properties of several measures of political discussion network characteristics. Political Behavior, 39(3), 585–608.

    Article  Google Scholar 

  • Malhotra, N., & Krosnick, J. A. (2007). The effect of survey mode and sampling on inferences about political attitudes and behavior: Comparing the 2000 and 2004 ANES to internet surveys with nonprobability samples. Political Analysis, 15(3), 286–323.

    Article  Google Scholar 

  • Mason, L. (2016). A cross-cutting calm: How social sorting drives affective polarization. Public Opinion Quarterly, 80(S1), 351–377.

    Article  Google Scholar 

  • Mason, L. (2018). Uncivil agreement: How politics became our identity. Chicago: University of Chicago Press.

    Book  Google Scholar 

  • McClurg, S. D. (2006). The electoral relevance of political talk: Examining disagreement and expertise effects in social networks on political participation. American Journal of Political Science, 50(3), 737–754.

    Article  Google Scholar 

  • McConnell, C., Margalit, Y., Malhotra, N., & Levendusky, M. (2018). The economic consequences of partisanship in a polarized era. American Journal of Political Science, 62(1), 5–18.

    Article  Google Scholar 

  • McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415–444.

    Article  Google Scholar 

  • Miller, W. E. (1956). One-party politics and the voter. American Political Science Review, 50(3), 707–725.

    Article  Google Scholar 

  • Mondak, J. J. (1990). Source cues and policy approval: The cognitive dynamcis of public support for the reagan agenda. American Journal of Political Science, 37, 186–212.

    Article  Google Scholar 

  • Mummolo, J., & Nall, C. (2017). Why partisans do not sort: The constraints on political segregation. Journal of Politics, 79(1), 45–59.

    Article  Google Scholar 

  • Mutz, D. C. (2002). Cross-cutting social networks: Testing democratic theory in practice. American Political Science Review, 96(1), 111–126.

    Article  Google Scholar 

  • Mutz, D. C. (2006). Hearing the other side: Deliberative versus participatory democracy. New York: Cambridge University Press.

    Book  Google Scholar 

  • Nall, C. (2015). The political consequences of spatial policies: How interstate highways facilitated geographic polarization. Journal of Politics, 77(2), 394–406.

    Article  Google Scholar 

  • Paluck, E. L., Green, S. A., & Green, D. P. (2019). The contact hypothesis re-evaluated. Behavioural Public Policy, 3(2), 129–158.

    Article  Google Scholar 

  • Papke, L. E., & Wooldridge, J. M. (1996). Econometric methods for fractional response variables with an application to 401(K) plan participation rates. Journal of Applied Econometrics, 11(6), 619–632.

    Article  Google Scholar 

  • Pettigrew, T. F. (1997). Generalized intergroup contact effects on prejudice. Personality and Social Psychology Bulletin, 23(2), 173–185.

    Article  Google Scholar 

  • Pettigrew, T. F. (1998). Intergroup contact theory. Annual Review of Psychology, 49(1), 65–85.

    Article  Google Scholar 

  • Popan, J. R., Kenworthy, J. B., Frame, M. C., Lyons, P. A., & Snuggs, S. J. (2010). Political groups in contact: The role of attributions for outgroup attitudes in reducing antipathy. European Journal of Social Psychology, 40(1), 86–104.

    Article  Google Scholar 

  • Prior, M. (2007). Post-broadcast democracy: How media choice increases inequality in political involvement and polarizes elections. New York: Cambridge University Press.

    Book  Google Scholar 

  • Robinson, R. J., Keltner, D., Ward, A., & Ross, L. (1995). Actual versus assumed differences in construal: “Naive Realism” in intergroup perception and conflict. Journal of Personality and Social Psychology, 68(3), 404–417.

    Article  Google Scholar 

  • Ross, L., Greene, D., & House, P. (1977). The ’False Consensus Effect’: An egocentric bias in social perception and attribution processes. Journal of Experimental Social Psychology, 13(3), 279–301.

    Article  Google Scholar 

  • Scala, D. J., & Johnson, K. M. (2017). Political polarization along the rural-urban continuum? The geography of the presidential vote, 2000–2016. The ANNALS of the American Academy of Political and Social Science, 672(1), 162–184.

    Article  Google Scholar 

  • Schmitt-Beck, R. (2003). Mass communication, personal communication and vote choice: The filter hypothesis of media influence in comparative perspective. British Journal of Political Science, 33(2), 233–259.

    Article  Google Scholar 

  • Settle, J. E. (2018). Frenemies: How social media polarizes America. New York: Cambridge University Press.

    Book  Google Scholar 

  • Sinclair, B. (2012). The social citizen: Peer networks and political behavior. Chicago, IL: Chicago University Press.

    Book  Google Scholar 

  • Sokhey, A. E., & Djupe, P. A. (2014). Name generation in interpersonal political network data: Results from a series of experiments. Social Networks, 36(1), 147–161.

    Article  Google Scholar 

  • Song, H., & Boomgaarden, H. G. (2017). Dynamic spirals put to test: An agent-based model of reinforcing spirals between selective exposure, interpersonal networks, and attitude polarization. Journal of Communication, 67(2), 256–281.

    Article  Google Scholar 

  • Sussell, J. (2013). New support for the big sort hypothesis: An assessment of partisan geographic sorting in California, 1992–2010. PS: Political Science & Politics, 46(4), 768–773.

    Google Scholar 

  • Taber, C. S., & Lodge, M. (2006). Motivated skepticism in the evaluation of political beliefs. American Journal of Political Science, 50(3), 755–769.

    Article  Google Scholar 

  • Tajfel, H., & Turner, J. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The social psychology of intergroup relations. Monterey, CA: Wadsworth.

    Google Scholar 

  • Cho, T., Wendy, K., Gimpel, J. G., & Hui, I. S. (2013). Voter migration and the geographic sorting of the American electorate. Annals of the Association of American Geographers, 103(4), 856–870.

    Article  Google Scholar 

  • Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.

    Article  Google Scholar 

  • Visser, P. S., & Mirabile, R. R. (2004). Attitudes in the social context: The impact of social network composition on individual-level attitude strength. Journal of Personality and Social Psychology, 87(6), 779–795.

    Article  Google Scholar 

  • Westfall, J., Van Boven, L., Chambers, J. R., & Judd, C. M. (2015). Perceiving political polarization in the United States: Party identity strength and attitude extremity exacerbate the perceived partisan divide. Perspectives on Psychological Science, 10(2), 145–158.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ross Butters.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

(PDF 420 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Butters, R., Hare, C. Polarized Networks? New Evidence on American Voters’ Political Discussion Networks. Polit Behav 44, 1079–1103 (2022). https://doi.org/10.1007/s11109-020-09647-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11109-020-09647-w

Keywords

  • Social networks
  • Disagreement
  • Polarization
  • Political perceptions
  • Political context