Social surveys have been widely used as a method of obtaining public opinion. Sometimes, it is more ideal to collect opinions by presenting questions in free-response formats than in multiple-choice formats. Despite their advantages, free-response questions are rarely used in practice because they usually require manual analysis. Therefore, classification of free-format texts can present a formidable task in large-scale surveys and can be influenced by the interpretation of analysts. In this study, we propose a network-based survey framework in which responses are automatically classified in a statistically principled manner. This can be achieved because, in addition to the text, similarities among responses are also assessed by each respondent. We demonstrate our approach using a poll on the 2016 US presidential election and a survey taken by graduates of a particular university. The proposed approach helps analysts interpret the underlying semantics of responses in large-scale surveys.
Access optionsAccess options
Subscribe to Journal
Get full journal access for 1 year
only $8.67 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The network datasets that support the findings of this study are available in a GitHub repository at https://github.com/tatsuro-kawamoto/opinion_graphs. The graph clustering code that supports the findings of this study is available in a GitHub repository at https://github.com/tatsuro-kawamoto/graphBIX.
Kahn, R. L. & Cannell, C. F. The Dynamics of Interviewing: Theory, Technique, and Cases (Wiley, 1957).
Schuman, H. & Scott, J. Problems in the use of survey questions to measure public opinion. Science 236, 957–959 (1987).
Schuman, H. & Presser, S. Questions and Answers in Attitude Surveys: Experiments on Question Form, Wording, and Context (Sage, 1996).
RePass, D. E. Issue salience and party choice. Am. Polit. Sci. Rev. 65, 389–400 (1971).
Kelley, S. Jr. Interpreting Eelections (Princeton Univ. Press, 2014).
Geer, J. G. What do open-ended questions measure? Public Opin. Q. 52, 365–371 (1988).
Singleton, R. & Straits, B. C. Approaches to Social Research. 6th edn (Oxford Univ. Press, 2017).
Schuman, H. The random probe: a technique for evaluating the validity of closed questions. Am. Sociol. Rev. 31, 218–222 (1966).
Lombard, M., Snyder-Duch, J. & Bracken, C. C. Content analysis in mass communication: assessment and reporting of intercoder reliability. Human Commun. Res. 28, 587–604 (2002).
Giddens, A. & Sutton, P. W. Sociology 7th edn (Polity Press, 2013).
Aicher, C., Jacobs, A. Z. & Clauset, A. Learning latent block structure in weighted networks. J. Complex Netw. 3, 221–248 (2015).
Newman, M. E. J. Network structure from rich but noisy data. Nat. Phys. 14, 542–545 (2018).
Peixoto, T. P. Reconstructing networks with unknown and heterogeneous errors. Phys. Rev. X 8, 041011 (2018).
Rosvall, M. & Bergstrom, C. T. Mapping change in large networks. PLoS ONE 5, 1–7 (2010).
Kawamoto, T. & Kabashima, Y. Comparative analysis on the selection of number of clusters in community detection. Phys. Rev. E 97, 022315 (2018).
Danon, L., Díaz-Guilera, A., Duch, J. & Arenas, A. Comparing community structure identification. J. Stat. Mech. 2005, P09008 (2005).
Rand, W. M. Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66, 846–850 (1971).
Hubert, L. & Arabie, P. Comparing partitions. J. Classif. 2, 193–218 (1985).
Simon, A. F. & Xenos, M. Dimensional reduction of word-frequency data as a substitute for intersubjective content analysis. Polit. Anal. 12, 63–75 (2004).
Hopkins, D. J. & King, G. A method of automated nonparametric content analysis for social science. Am. J. Polit. Sci. 54, 229–247 (2010).
Roberts, M. E. et al. Structural topic models for open-ended survey responses. Am. J. Polit. Sci. 58, 1064–1082 (2014).
Benoit, K., Conway, D., Lauderdale, B. E., Laver, M. & Mikhaylov, S. Crowd-sourced text analysis: reproducible and agile production of political data. Am. Polit. Sci. Rev. 110, 278–295 (2016).
Lind, F., Gruber, M. & Boomgaarden, H. G. Content analysis by the crowd: assessing the usability of crowdsourcing for coding latent constructs. Commun. Methods Meas. 11, 191–209 (2017).
Jacobson, M. R., Whyte, C. E. & Azzam, T. Using crowdsourcing to code open-ended responses: a mixed methods approach. Am. J. Eval. 39, 413–429 (2018).
Decelle, A., Krzakala, F., Moore, C. & Zdeborová, L. Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications. Phys. Rev. E 84, 066106 (2011).
Moore, C. The computer science and physics of community detection: landscapes, phase transitions, and hardness. Preprint at https://arxiv.org/abs/1702.00467 (2017).
Fishkin, J. S. When the People Speak: Deliberative Democracy and Public Consultation (Oxford Univ. Press, 2011).
Holland, P. W., Laskey, K. B. & Leinhardt, S. Stochastic blockmodels: first steps. Soc. Netw. 5, 109–137 (1983).
Wang, Y. J. & Wong, G. Y. Stochastic blockmodels for directed graphs. J. Am. Stat. Assoc. 82, 8–19 (1987).
Karrer, B. & Newman, M. E. J. Stochastic blockmodels and community structure in networks. Phys. Rev. E 83, 016107 (2011).
Bishop, C. M. Pattern Recognition and Machine Learning (Information Science and Statistics) (Springer, 2006).
Mézard, M. & Montanari, A. Information, Physics, and Computation (Oxford Univ. Press, 2009).
Decelle, A., Krzakala, F., Moore, C. & Zdeborová, L. Inference and phase transitions in the detection of modules in sparse networks. Phys. Rev. Lett. 107, 065701 (2011).
Kawamoto, T. Algorithmic detectability threshold of the stochastic block model. Phys. Rev. E 97, 032301 (2018).
Abbe, E. Community detection and stochastic block models: recent developments. Preprint at https://arxiv.org/abs/1703.10146 (2017).
Peixoto, T. P. Bayesian stochastic blockmodeling. Preprint at https://arxiv.org/abs/1705.10225 (2017).
Kawamoto, T. Algorithmic infeasibility of community detection in higher-order networks. Preprint at https://arxiv.org/abs/1710.08816 (2017).
Kawamoto, T. & Kabashima, Y. Cross-validation estimate of the number of clusters in a network. Sci. Rep. 7, 3327 (2017).
The authors thank H. Tokioka and S. Shinomoto for discussions. The authors are also grateful to J. Park and M. Rosvall for their comments. Finally, the authors appreciate all the people who contributed to the poll on the 2016 US presidential election and acknowledge support from the Faculty of Education in Kagawa University and the reunion of the faculty. T.K. was supported by JSPS (Japan) KAKENHI grant no. 26011023. T.A. was supported by the Research Institute for Mathematical Sciences, a joint research centre at Kyoto University, and open collaborative research at the National Institute of Informatics (NII) Japan (FY2017). T.K. and T.A. acknowledge financial support from JSPS KAKENHI grant no. 18K18604.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–9 and Supplementary Tables 1–3