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From Respondents to Networks: Bridging Between Individuals, Discussants, and the Network in the Study of Political Discussion

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Abstract

Much of our understanding of social influence in individual political behavior stems from representative surveys asking respondents to identify characteristics of a small number of people they talk to most frequently. By focusing only on these few close contacts, we have implicitly assumed that less-intimate associates and features of network structure hold little influence over others’ attitudes and behavior. We test these assumptions with a survey that attempted to interview all students at a small university during a highly-salient municipal election. By focusing on a small, well-defined community, we are able to explore the relationship between individuals, their close associates, and also less-immediate associates. We are also able to explore features of network structure unobtainable in representative samples. We demonstrate that these less-immediate associates and network features have the potential to exert important influence that conventional survey approaches would miss.

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Notes

  1. The study was approved by the College of William & Mary IRB on Human Subjects, PHSC-2010-03-04-6495-rbrapo.

  2. In total, 1833 of the 5836 undergraduates provided at least one friend’s full name in the name generator, yielding an AAPOR non-probability internet panel participation rate of 31% (The American Association for Public Opinion Research 2015, 40).

  3. We rely on a database produced by the Williamsburg Voter Registrar including all registered voters, their date of registration, date of birth, address, and voting history. We used first and last names to find all potential matches between individuals in our student sample and those registered to vote.  In each case, we used both the date of birth as well as the address to help confirm that a matched name corresponded to a student. According to Ansolabehere and Hersh (2010) summary measure, Virginia maintains some of the highest-quality registration records in the US.

  4. Indegree provides a better measure of global centrality than either of its variants: outdegree and total degree. In this case, outdegree is equal to the number of people an individual names as friends. Outdegree is thus limited only to survey respondents and provides little variation (as described above, 78% of students responding to the name generator identified the maximum five friends). Total degree is also problematic because it equals the sum of indegree and outdegree. For non-respondents, this sum is always the same as indegree, but for respondents, it is an average of 4.6 units greater than their indegree. Thus, total degree conflates popularity and survey participation.

  5. Alternatively, the local clustering coefficient would provide a measure of the uniqueness or redundancy of friends. We rely instead on the two-step neighborhood density because it captures uniqueness or redundancy of not only friends (Zone 1), but also second-order friends (Zone 2).

  6. All tables in this manuscript were originally typeset using the texreg package in R (Leifeld 2013).

  7. This indicator variable is constructed from an item asking, “Have you ever attended an off-campus party where the police issued a citation to you or someone else at that party for violating the noise ordinance?”.

  8. This and all other reported predictions and confidence intervals come from simulations from the posterior (Gelman and Hill 2007), setting other covariates to their medians.

  9. These means are equal to the number of friends in the zone who state that they have been to a party cited by police for violating the noise ordinance, divided by the number of friends in the zone.

  10. The other controls are the respondent’s interest in national politics (“In general how interested are you in national politics?”), family economic status (“How would you describe your family's economic status?”), and indicators of race and gender.

  11. Zone 1’s mean turnout is equal to the number of validated voters in the respondent’s Zone 1 network, divided by the total number of friends in Zone 1. An analogous Zone 2 measure is introduced in Table 4, Model 3.

References

  • Ansolabehere, S., & Hersh, E. (2010). The quality of voter registration records: A state-by-state analysis. Cambridge, MA: Caltech/MIT Voting Technology Project Report. http://www.vote.caltech.edu/content/qualityvoter-registration-records-state-state-analysis.

  • Ansolabehere, S., & Hersh, E. (2012). Validation: What big data reveal about survey misreporting and the real electorate. Political Analysis, 20(4), 437–459.

    Article  Google Scholar 

  • Berelson, B. F., Lazarsfeld, P. F., & McPhee, W. N. (1954). Voting. Chicago: University of Chicago Press.

    Google Scholar 

  • Burden, B. C. (2000). Voter turnout and the national election studies. Political Analysis, 8(4), 389–398.

    Article  Google Scholar 

  • Burt, R. S. (1986). A note on sociometric order in the General Social Survey Network Data. Social Networks, 8, 149–174.

    Article  Google Scholar 

  • Campbell, A., Converse, P. E., Miller, W. E., & Stokes, D. E. (1960). The American voter. New York: Wiley.

    Google Scholar 

  • Centola, D. (2010). The spread of behavior in an Online Social Network Experiment. Science, 329(5996), 1194–1197.

    Article  Google Scholar 

  • Centola, D., & Macy, M. (2007). Complex contagions and the weakness of long ties. American Journal of Sociology, 113(3), 702–734.

    Article  Google Scholar 

  • Christakis, N. A., & Fowler, J. H. (2008). The collective dynamics of smoking in a Large Social Network. New England Journal of Medicine, 358(21), 2249–2258.

    Article  Google Scholar 

  • Costenbader, E., & Valente, T. W. (2003). The stability of centrality measures when networks are sampled. Social Networks, 25(4), 283–307.

    Article  Google Scholar 

  • Eveland, W. 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, W. P., & Kleinman, S. B. (2011). Comparing general and political discussion networks within voluntary organizations using social network analysis. Political Behavior, 35(1), 65–87.

    Article  Google Scholar 

  • Fowler, J. H., et al. (2011). Causality in political networks. American Politics Research, 39(2), 437–480.

    Article  Google Scholar 

  • Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press.

    Google Scholar 

  • Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Groves, R. M., Presser, S., & Dipko, S. (2004). The role of topic interest in survey participation decisions. Public Opinion Quarterly, 68(1), 2–31.

    Article  Google Scholar 

  • Hill, R. A., & Dunbar, R. I. M. (2003). Social network size in humans. Human Nature, 14(1), 53–72.

    Article  Google Scholar 

  • Huckfeldt, R. (1979). Political participation and the neighborhood social context. American Journal of Political Science, 23, 579–592.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Huckfeldt, R., Ikeda, K., & Pappi, F. (2005). Patterns of disagreement in democratic politics: Comparing Germany, Japan, and the United States. American Journal of Political Science, 49, 497–514.

    Article  Google Scholar 

  • Huckfeldt, R., Johnson, P. E., & Sprague, J. (2002). Political environments, political dynamics, and the survival of disagreement. The Journal of Politics, 64(01), 1–21.

    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 

  • Huckfeldt, R., & Mendez, J. M. (2008). Moths, flames, and political engagement: Managing disagreement within communication networks. The Journal of Politics, 70(01), 83–96.

    Article  Google Scholar 

  • Huckfeldt, R., & Sprague, J. (1995). Citizens, politics, and social communication. New York: Cambridge University Press.

    Book  Google Scholar 

  • Hurwitz, J., & Peffley, M. (1987). How are foreign policy attitudes structured? A hierarchical model. American Political Science Review, 81(04), 1099–1120.

    Article  Google Scholar 

  • Jan, E. L., & Nagler, J. (2013). Who votes now?: Demographics, issues, inequality, and turnout in the United States. New Jersey: Princeton University Press.

    Google Scholar 

  • Key, V. O., Jr. (1949). Southern politics. New York: Knopf.

    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 

  • Langton, K. P., & Rapoport, R. (1975). Social structure, social context, and partisan mobilization: Urban workers in Chile. Comparative Political Studies, 8, 318–344.

    Article  Google Scholar 

  • Laumann, E. O. (1973). Bonds of Pluralism. New York: Wiley.

    Google Scholar 

  • Lazarsfeld, P. F., Berelson, B. F., & Gaudet, H. (1948). The people’s choice. New York: Columbia University Press.

    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 

  • Leifeld, P. (2013). Texreg: Conversion of Statistical Model Output in R to \LaTeX and HTML tables. Journal of Statistical Software, 55(8), 1–24.

    Article  Google Scholar 

  • Leighley, J. E., & Nagler, J. (1992). Individual and systemic influences on turnout: Who votes? 1984. The Journal of Politics, 54(03), 718–740.

    Article  Google Scholar 

  • Levitan, L. C., & Visser, P. S. (2009). Social network composition and attitude strength: Exploring the dynamics within newly formed social networks. Journal of Experimental Social Psychology, 45(5), 1057–1067.

    Article  Google Scholar 

  • Long, J. S., & Freese, J. (2014). Regression models for categorical dependent variables using stata (3rd ed.). College Station, TX: Stata Press.

    Google Scholar 

  • Louch, H. (2000). Personal network integration: Transitivity and homophily in strong-tie relations. Social Networks, 22(1), 45–64.

    Article  Google Scholar 

  • Lyons, J., & Sokhey, A. (2014). Emotion, motivation, and social information seeking about politics. Political Communication, 31(2), 237–258.

    Article  Google Scholar 

  • Marsden, P. V. (1987). Core discussion networks of Americans. American Sociological Review, 52, 122–131.

    Article  Google Scholar 

  • Marsden, P. V. (2003). Interviewer effects in measuring network size using a single name generator. Social Networks, 25(1), 1–16.

    Article  Google Scholar 

  • Matthews, D. R., & Prothro, J. W. (1963). Social and economic factors and Negro voter registration in the South. American Political Science Review, 57, 24–44.

    Article  Google Scholar 

  • McClurg, S. D. (2003). Social networks and political participation: The role of social interaction in explaining political participation. Political Research Quarterly, 56, 448–465.

    Article  Google Scholar 

  • McClurg, S. (2006). Political disagreement in context: The conditional effect of neighborhood context, disagreement and political talk on electoral participation. Political Behavior, 28(4), 349–366.

    Article  Google Scholar 

  • Nickerson, D. W. (2008). Is voting contagious? Evidence from two field experiments. American Political Science Review, 102(01), 49–57.

    Article  Google Scholar 

  • Nyhan, B., & Reifler, J. (2010). When corrections fail: The persistence of political misperceptions. Political Behavior, 32(2), 303–330.

    Article  Google Scholar 

  • O’Connor, K. M., & Gladstone, E. (2015). How social exclusion distorts social network perceptions. Social Networks, 40, 123–128.

    Article  Google Scholar 

  • Redlawsk, D. P., Civettini, A. J. W., & Emmerson, K. M. (2010). The affective tipping point: Do motivated reasoners ever ‘get it’?: The affective tipping point. Political Psychology, 31(4), 563–593.

    Article  Google Scholar 

  • Roberts, S. G. B., Dunbar, R. I. M., Pollet, T. V., & Kuppens, T. (2009). Exploring variation in active network size: Constraints and ego characteristics. Social Networks, 31(2), 138–146.

    Article  Google Scholar 

  • Ryan, J. B. (2010). The effects of network expertise and biases on vote choice. Political Communication, 27(1), 44–58.

    Article  Google Scholar 

  • Ryan, J. B. (2011). Social networks as a shortcut to correct voting. American Journal of Political Science, 55(4), 753–766.

    Article  Google Scholar 

  • Selb, P., & Munzert, S. (2013). Voter overrepresentation, vote misreporting, and turnout bias in postelection surveys. Electoral Studies, 32(1), 186–196.

    Article  Google Scholar 

  • Simpson, B., Markovsky, B., & Steketee, M. (2011). Power and the perception of social networks. Social Networks, 33(2), 166–171.

    Article  Google Scholar 

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

    Book  Google Scholar 

  • Sinclair, B., McConnell, M., & Green, D. P. (2012). Detecting spillover effects: Design and analysis of multilevel experiments. American Journal of Political Science, 56(4), 1055–1069.

    Article  Google Scholar 

  • Sokhey, A. E., & McClurg, S. D. (2012). Social networks and correct voting. The Journal of Politics, 74(03), 751–764.

    Article  Google Scholar 

  • Song, H., & Eveland, W. P. (2015). The structure of communication networks matters: How network diversity, centrality, and context influence political ambivalence, participation, and knowledge. Political Communication, 32(1), 83–108.

    Article  Google Scholar 

  • Tingsten, H. (1963). Political Behavior: Studies in Election Statistics. London: P.S. King.

    Google Scholar 

  • VanderWeele, T. J. (2011). Sensitivity analysis for contagion effects in social networks. Sociological Methods & Research, 40(2), 240–255.

    Article  Google Scholar 

  • Walsh, K. C. (2004). Talking about politics: Informal groups and social identity in American life. Chicago: University of Chicago Press.

    Google Scholar 

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.

    Article  Google Scholar 

  • Wolfinger, R. E., & Rosenstone, S. J. (1980). Who votes?. New Haven: Yale.

    Google Scholar 

  • Zaller, J. (1992). The nature and origins of mass opinion. Cambridge: Cambridge University Press.

    Book  Google Scholar 

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Author information

Authors and Affiliations

  1. Florida State University, Tallahassee, USA

    Matthew T. Pietryka

  2. New College of Florida, Sarasota, USA

    Jack Lyons Reilly

  3. College of William & Mary, Williamsburg, USA

    Daniel M. Maliniak & Ronald B. Rapoport

  4. University of Kansas, Lawrence, USA

    Patrick R. Miller

  5. University of California, Davis, USA

    Robert Huckfeldt

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  1. Matthew T. Pietryka
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Corresponding author

Correspondence to Matthew T. Pietryka.

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Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Additional information

We thank Dennis Langley and Jessica Parsons for research assistance and Quintin Beazer and Scott McClurg for helpful comments. Our data and replication files can be downloaded from the Political Behavior Dataverse: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GUAY8P.

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Pietryka, M.T., Reilly, J.L., Maliniak, D.M. et al. From Respondents to Networks: Bridging Between Individuals, Discussants, and the Network in the Study of Political Discussion. Polit Behav 40, 711–735 (2018). https://doi.org/10.1007/s11109-017-9419-3

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