Table 3 reports raw proportions of characteristics and self-reported behaviors across various sample definitions. Knowledge ranged from 0 to 4 and was constructed from a grid of questions about the majority party in the House and Senate, in addition to questions about whether the uninsured rate and earnings had increased over the course of 2016. Voter turnout was verified by our survey provider, which matches individual respondents to the TargetSmart voter file.

FB, Facebook.

A potential concern about sample-selection bias is that those who consented to share Facebook data were different from the rest of the sample along some dimension that is also related to our outcome of interest (fake news sharing behavior). Table 3 suggests that, at least on closely related observable characteristics, the subgroup for which we have profile data is a valid cross section of the overall sample. In particular, frequent self-reported Facebook sharing activity is roughly indistinguishable between those who report having a Facebook account and those who provided access (P = 0.28). The samples are also comparable on age, frequency of looking at Facebook, and vote intention. Those who shared data were slightly more liberal on average (P = 0.01), but we controlled for this in our models and we expected differences between the samples to arise due to chance alone. Last, it may not be surprising that those who provided access to profile data were also more likely to participate in elections, as measured by verified voter turnout in the 2016 general election. We see this somewhat heightened political engagement in the Facebook subsample as important to note, and we accounted for the effects of this difference when we controlled for overall posting activity in our models.

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