Democratic classification of free-format survey responses with a network-based framework


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.

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Fig. 1: Schematic of our network-based approach.
Fig. 2: Results of the poll on the 2016 US presidential election.
Fig. 3: Results of the survey focusing on graduates of the Faculty of Education.

Data availability

The network datasets that support the findings of this study are available in a GitHub repository at The graph clustering code that supports the findings of this study is available in a GitHub repository at


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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.

Author information

T.K. and T.A. designed the survey framework, analysed the data and wrote the manuscript. T.K. implemented the online survey system. T.K. conducted a survey of the poll on the 2016 US presidential election and T.A. mainly conducted a survey focusing on graduates of the Faculty of Education of a particular university.

Correspondence to Tatsuro Kawamoto.

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Supplementary Figs. 1–9 and Supplementary Tables 1–3

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