Abstract
We examine the extent to which Republican members of the House of Representatives have attempted to delegitimize established media by adopting the fake news label on Twitter since Donald Trump’s election. We find that a significant minority of Republican representatives used the fake news label on Twitter. Ideology, measured through roll-call voting behavior (DW-NOMINATE), was the strongest indicator of likely adoption, with conservative representatives using the label at significantly higher rates than comparative moderates. Quantity of tweets sent was a further significant predictor of use, with active Twitter users more disposed to use the label on the platform. District partisanship (PVI) provided no explanatory value beyond ideology, suggesting limited ‘tactical’ use of the label for electoral gain. We discuss potential reasons for these findings and consider consequences for various actors, including Trump. We respond to a call in the literature for more empirical data concerning the use of the fake news label by actors other than President Trump by assessing the extent to which House Republicans have adopted this behavior.
About the authors
Mike Cowburn is a Ph.D. candidate at the Graduate School of North American Studies at the Freie Universität Berlin specializing in Congress, Party Factions, Elections and Nomination Systems. His Ph.D. project examines changes in the dynamics of congressional primary competitions in the 21st century with specific focus on the electoral consequences of ideological contests.
Michael T. Oswald is an assistant professor of political science at the University of Passau, associate research fellow and lecturer at the John F. Kennedy Institute of the Freie Universität Berlin and faculty member at International Center for European Education. He wrote his dissertation on the Tea Party movement and has held visiting scholarships at Texas A&M and Harvard University.
Appendix Goodness of fit for logistic regression model
Logistic model for YesDummy
| — True — | |||
|---|---|---|---|
| Classified | D | ∼D | Total |
| + | 23 | 10 | 33 |
| – | 44 | 190 | 234 |
| Total | 67 | 200 | 267 |
Classified + if predicted Pr(D) ≥ 0.5.
True D defined as YesDummy ! = 0.
| Sensitivity | Pr(+|D) | 34.33% |
| Specificity | Pr(−|∼D) | 95.00% |
| Positive predictive value | Pr(D|+) | 69.70% |
| Negative predictive value | Pr(∼D|−) | 81.20% |
| False + rate for true ™D | Pr(+|∼D) | 5.00% |
| False − rate for true D | Pr(−|D) | 65.67% |
| False + rate for classified + | Pr(∼D|+) | 30.30% |
| False − rate for classified − | Pr(D|−) | 18.800% |
| Correctly classified | 79.78% |
| Logistic model for YesDummy, goodness-of-fit test | |
|---|---|
| Number of observations = | 267 |
| Number of covariate patterns = | 267 |
| Pearson chi2 (259) = | 286.91 |
| Prob > chi2 = | 0.1124 |
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