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How Polarized are Online Conversations about Childhood?

Breanna E. Green, William R. Hobbs

TL;DR

The paper investigates whether online conversations about childhood are polarized by political affiliation, especially in the context of the COVID-19 era. It combines moral foundations analysis using the Moral Foundations Dictionary 2.0, fightin' words, and conText-style embedding methods on a Twitter panel linked to voter records from 2019–2023, spanning Democratic and Republican perspectives and including Florida and New York as case studies. The main finding is that most child-related discussions are not strongly polarized by party; when differences exist, they align with pre-existing partisan debates (e.g., race, health, religion) and are more pronounced in education and pandemic topics, with Florida showing greater event-linked polarization than New York. The work highlights that polarization around childhood largely reflects broader partisan conflicts rather than novel concerns about child welfare, offering implications for communication strategies that seek to depolarize discussions and emphasize common ground in parenting and child welfare issues.

Abstract

2020 through 2023 were unusually tumultuous years for children in the United States, and children's welfare was prominent in political debate. Theories in moral psychology suggest that political parties would treat concerns for children using different moral frames, and that moral conflict might drive substantial polarization in discussions about children. However, such partisan frames may still differ very little if there is limited underlying disagreement about moral issues and everyday concerns in childhood when not explicitly referencing politics. We evaluate claims of universality and division in moral language using tweets from 2019-2023 linked to U.S. voter records, focusing on expressed morality. Our results show that mentions of children by Republicans and Democrats are usually similar, differing no more than mentions by women and men, and tend to contain no large differences in accompanying moral words. To the extent that mentions of children did differ across parties, these differences were constrained to topics polarized well before the pandemic -- and slightly heightened when co-mentioned with `kids' or `children'. These topics reflected a small fraction of conversations about children. Overall, polarization of online discussion around childhood appears to reflect escalated polarization on lines of existing partisan conflicts rather than concerns originating from new concerns about the welfare of children during and after the pandemic.

How Polarized are Online Conversations about Childhood?

TL;DR

The paper investigates whether online conversations about childhood are polarized by political affiliation, especially in the context of the COVID-19 era. It combines moral foundations analysis using the Moral Foundations Dictionary 2.0, fightin' words, and conText-style embedding methods on a Twitter panel linked to voter records from 2019–2023, spanning Democratic and Republican perspectives and including Florida and New York as case studies. The main finding is that most child-related discussions are not strongly polarized by party; when differences exist, they align with pre-existing partisan debates (e.g., race, health, religion) and are more pronounced in education and pandemic topics, with Florida showing greater event-linked polarization than New York. The work highlights that polarization around childhood largely reflects broader partisan conflicts rather than novel concerns about child welfare, offering implications for communication strategies that seek to depolarize discussions and emphasize common ground in parenting and child welfare issues.

Abstract

2020 through 2023 were unusually tumultuous years for children in the United States, and children's welfare was prominent in political debate. Theories in moral psychology suggest that political parties would treat concerns for children using different moral frames, and that moral conflict might drive substantial polarization in discussions about children. However, such partisan frames may still differ very little if there is limited underlying disagreement about moral issues and everyday concerns in childhood when not explicitly referencing politics. We evaluate claims of universality and division in moral language using tweets from 2019-2023 linked to U.S. voter records, focusing on expressed morality. Our results show that mentions of children by Republicans and Democrats are usually similar, differing no more than mentions by women and men, and tend to contain no large differences in accompanying moral words. To the extent that mentions of children did differ across parties, these differences were constrained to topics polarized well before the pandemic -- and slightly heightened when co-mentioned with `kids' or `children'. These topics reflected a small fraction of conversations about children. Overall, polarization of online discussion around childhood appears to reflect escalated polarization on lines of existing partisan conflicts rather than concerns originating from new concerns about the welfare of children during and after the pandemic.
Paper Structure (25 sections, 5 figures, 1 table)

This paper contains 25 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Why online conversations about children might not polarize
  4. Morality, politics, and children.
  5. Online social media and political conversations.
  6. Why online conversations about children might (sometimes) polarize
  7. Pandemic and culture war politics: through subsets of conversations and over time in Florida.
  8. Data
  9. Partisanship and gender.
  10. Text pre-processing.
  11. Methods
  12. Moral foundation dictionaries and party differences in moral language use.
  13. Variation in partisan language use by keyword/topic.
  14. Largest term-level party differences in moral foundations.
  15. Polarization in online conversations about children.
  16. ...and 10 more sections

Figures (5)

  • Figure 1: Party moral differences, controlling for differences in tweet length. See text and Figure \ref{['fig:emb_by_term']} for the lists of keywords by category.
  • Figure 2: Embedding Regression by Term. In this figure, the solid lines indicates the facet (e.g., facet "Education keywords") term-frequency weighted average for terms children/kids and the dotted lines facet weighted averages for people. Numbers under each term indicate the number of users within the 10% sample who used that term at least once, and who are included in the term's embedding regression. Gray bars indicate the 95% confidence intervals for the null distribution of each estimate. These distance estimates can have negative values (see Methods section).
  • Figure 3: Partisan differences in language use when discussing children and kids: Florida. Vertical lines indicate the start of the COVID-19 pandemic (orange), the murder of George Floyd (green), the inauguration of Joe Biden as US president (blue), and the FDA's emergency use authorization for the COVID-19 vaccine in children aged 12 to 15 in May 2021 (purple). The horizontal black line indicates the average level of partisan difference in language use when mentioning people. Vertical gray bars are 95% confidence intervals for estimates' monthly null distributions from permutation tests. Months September through December 2022 are missing due to data collection problems during that period.
  • Figure 4: Partisan differences in language use when discussing children and kids -- this figure repeats the analysis in Figure \ref{['fig:ny_kid']} for the state of New York.
  • Figure A1: Party moral differences for mentions of people, controlling for differences in tweet length. See text and Figure \ref{['fig:emb_by_term']} for the lists of keywords by category. Due to computational limitations, we took a further 50% sample of users for this analysis (multiply the sample sizes here by 2 for a frequency comparison with mentions of children/kids).