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.
Details on main BuzzFeed-based list
Our BuzzFeed-based list began with 30 domains identified by that site’s reporting as purveyors of intentionally false election-related stories generating the most Facebook engagement. To do this, the journalists, led by Craig Silverman, used keywords and existing lists combined with the analytics service BuzzSumo. To ensure that our analysis stayed clear of websites that could be construed as partisan or hyperpartisan (rather than intentionally or systematically factually inaccurate), we additionally filtered out domains identified as hard news by a supervised learning classifier developed by Bakshy et al. (23). The nearly 500 hard news domains encompass a wide range of news and opinion websites, both mainstream and niche. The classifier was trained on the text features of roughly 7 million web pages shared on Facebook over a 6-month period by U.S. users, with training labels for hard and soft news generated using bootstrapped keyword searches on the URLs. Once matches to this list of hard news domains were removed (for example, Breitbart.com), we were left with 21 domains, shown below.
(1) usanewsflash.com
(2) abcnews.com.co
(4) rickwells.us
(5) truepundit.com
(10) conservativedailypost.com
(13) endingthefed.com
(14) donaldtrumpnews.co.
(15) yesimright.com
(17) bizstandardnews.com
(18) everynewshere.com
(19) departed.co.
(21) tmzhiphop.com
Sample details
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.
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.
SUPPLEMENTARY MATERIALS
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/1/eaau4586/DC1
Tables S1–S13. Determinants of fake news sharing on Facebook (alternate specification).
Table S14. Determinants of hard news sharing on Facebook.
Fig. S1. Average number of fake news articles shared by age group (with 95% confidence intervals), using the URL-level measure derived from (2).
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
More Articles
- Encountering the News From the British Library's Breaking the News Exhibition: Unsettling, But Exciting
- Jo Freeman Reviews: Gendered Citizenship: The Original Conflict Over the Equal Rights Amendment, 1920 – 1963
- Senate Commerce Subcommittee Set ... Protecting Kids Online: Testimony From a Facebook Whistleblower
- Rose Madeline Mula Writes: To Drive or Not To Drive — That Is The Question
- “The Trump-Ukraine Impeachment Inquiry Report” – House Permanent Select Committee on Intelligence Released the Draft Report to All Members and the Public
- Beware the Fashion Flim-Flammers
- Obamacare Exchanges In Limbo; Deadlines Fast Approaching for the Start of Open Enrollment this Fall
- Elaine Soloway's Rookie Widow Series: Leaving Home, My Magic Act and The Gold Line to South Pasadena
- Fact Tank: Voters Have Little Confidence Clinton or Trump Would Help Workers Get Skills They Need to Compete
- Get Ready, Political Fans: Convention Facts for the GOP