SubPOP dataset is a subpopulation-level opinion distribution dataset derived from a limited portion of public opinion survey questions from Pew Research Center's American Trends Panel and NORC at the University of Chicago's General Social Survey (2022 cross-sectional survey).

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Acknowledge the source of the data with express reference to the owner of the data, in accordance with the following citation: 'Pew Research Center’s American Trends Panel', 'Davern, Michael; Bautista, Rene; Freese, Jeremy; Herd, Pamela; and Morgan, Stephen L. General Social Survey 1972-2022. [Machine-readable data file]. Principal Investigator, Michael Davern; Co-Principal Investigators, Rene Bautista, Jeremy Freese, Pamela Herd, and Stephen L. Morgan. NORC ed. Chicago, 2024. 1 datafile and 1 codebook (2022 Release 3a).'
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Include the following disclaimer: “The opinions expressed herein, including any implications for policy, are those of the author and not of the survey research centers.”
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Dataset Card for SubPOP

The dataset is from the paper, Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions by Joseph Suh, Erfan Jahanparast, Suhong Moon, Minwoo Kang, and Serina Chang.

Dataset Summary

The dataset contains a subset of survey questions about social issues and opinions in the United States adapted from American Trends Panel and General Social Survey.

It consists of two parts, SubPOP-Train and SubPOP-Eval, which are derived from American Trends Panel and General Social Survey, respectively.

Questions. We include 3,229 questions for SubPOP-Train, and 133 questions for SubPOP-Eval.

Subpopulations. Following OpinionQA dataset, we include 22 subpopulations across 8 attributes: region, income, education, race/ethnicity, gender, religion, political party affiliation, and political ideology.

In our paper, we use this dataset to evaluate the large language models' capability to predict the subpopulation-level opinion response distributions.

File Structure

Each list element in the files subpop_train.jsonl and subpop_eval.jsonl is a dictionary corresponding to a subpopulation's response distribution to a survey question. The dictionary items are:

qkey (str): the question's ID, adopted from the original survey data.
attribute (str): demographic or ideology variable defining the subpopulation, such as CREGION (region), POLPARTY (political party affiliation).
group (str): subpopulation, such as Northeast (population living in the U.S. Northeast), Democrat (population identified as Democrat).
question (str): the survey question string.
options (List[str]): the list of option choices presented at the time of survey.
ordinal (List[float]): ordinal structure of the option choices. Following OpinionQA, we include the ordinal structure for SubPOP-Eval. For example, a list of options Strongly agree, Agree, Neutral, Disagree, Strongly disagree is mapped to floating point number 1.0 through 5.0.
responses (List[float]): the response distribution of the group to the question. It is a post-stratification weighted distribution, and values in the list are normalized to a sum of 1.0. For more information about weighting, please refer to For Weighting Online Opt-in Samples, What Matters Most?.
refusal_rate (float): the fraction of respondents who selected 'Refused to answer' choice at the time of survey.

The dataset is further described in our paper and the GitHub repository.

Citation

@article{suh2025language,
  title={Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions},
  author={Suh, Joseph and Jahanparast, Erfan and Moon, Suhong and Kang, Minwoo and Chang, Serina},
  journal={arXiv preprint arXiv:2502.16761},
  year={2025}
}

Contact

josephsuh@berkeley.edu

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