Modeling strategy and covariates

We aggregated all shares to the individual respondent level so that our dependent variables are counts (i.e., number of fake news stories shared). To account for this feature of the data, as well as the highly skewed distribution of the counts, we primarily used Poisson or quasi-Poisson regressions to model the determinants of Facebook sharing behavior. We conducted dispersion tests on the count data and used quasi-Poisson models if the null hypothesis of no dispersion is rejected. Below, we included negative binomial and Ordinary Least Squares (OLS) regressions to show that our results are generally not sensitive to model choice. All models applied weights from YouGov to adjust for selection into the sample. We specifically used sample-matching weights produced for the third wave of the survey, which was closest to the Facebook encouragement sent to respondents (27). (Results also do not appear to be sensitive to the use of weights.)

We included a mix of relevant sociodemographic and political variables as predictors. These include age (reference category, 18 to 29), race, gender, family income, and educational attainment. In all models, we included either five-point ideological self-placement, three-point party identification, or both. Since these variables were correlated (r = 0.31), we addressed possible multicollinearity via transparency—we provided our main results all three ways. (In all models, the reference case for party identification and ideology is “Not sure.” Specifications including additional racial/ethnic categories are statistically and substantively unchanged; available from the authors.) Last, we included a measure of the total number of wall posts including a URL. This is intended to capture the overall level of respondents’ Facebook link-sharing activity regardless of political content or verifiability.

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