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

Political Behavior (2020)Cite this article

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.

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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.

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    Ross Butters & Christopher Hare

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Butters, R., Hare, C. Polarized Networks? New Evidence on American Voters’ Political Discussion Networks. Polit Behav (2020). https://doi.org/10.1007/s11109-020-09647-w

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Keywords

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