Values from the “fake information”
To answer you to matter, i once more analyzed the latest responses victims provided whenever expected what fake information and you may propaganda mean. I analyzed just those solutions in which victims considering a meaning to possess either label (55%, n = 162). Remember that the fresh new proportion away from subjects which offered for example significance was less than when you look at the Tests step 1 (95%) and you may 2 (88%). Through to nearer test, i discovered that numerous victims had more than likely pasted significance off a keen Internet search. Inside an enthusiastic exploratory data, i receive a mathematically significant difference from the probability you to definitely users offered a great pasted definition, according to https://hookupdaddy.net/bbw-hookup/ Governmental Personality, ? dos (dos, N = 162) = seven.66, p = 0.022. Specifically, conservatives (23%) was in fact probably be than just centrists (6%) to provide an effective pasted meaning, ? 2 (step one, N = 138) = seven.29, p = 0.007, Or = cuatro.57, 95% CI [step one.29, ], various other p viewpoints > 0.256. Liberals decrease between such extremes, having 13% delivering good pasted meaning. Because the we had been seeking subjects’ very own meanings, i omitted this type of skeptical responses from data (letter = 27).
We followed an equivalent analytical processes like in Experiments step 1 and you will 2. Dining table 4 screens this type of data. As the table shows, the brand new size of subjects whoever responses integrated the features explained in Check out step 1 had been equivalent round the governmental identification. Particularly, i did not replicate the latest shopping for of Try 1, in which individuals who identified left was basically very likely to promote separate meanings to the terminology than those who identified best, ? dos (1, N = 90) = step 1.42, p = 0.233, some other p values > 0.063.
Most exploratory analyses
We now turn to our additional exploratory analyses specific to this experiment. First, we examine the extent to which people’s reported familiarity with our news sources varies according to their political identification. Liberals and conservatives iliar with different sources, and we know that familiarity can act as a guide in determining what is true (Alter and Oppenheimer 2009). To examine this idea, we ran a two-way Ailiarity, treating Political Identification as a between-subjects factor with three levels (Left, Center, Right) and News Source as a within-subject factor with 42 levels (i.e., Table 1). This analysis showed that the influence of political identification on subjects’ familiarity ratings differed across the sources: F(2, 82) = 2.11, p < 0.001, ? 2 = 0.01. Closer inspection revealed that conservatives reported higher familiarity than liberals for most news sources, with centrists falling in-between (Fs range 6.62-, MRight-Remaining range 0.62-1.39, all p values < 0.002). The exceptions-that is, where familiarity ratings were not meaningfully different across political identification-were the media giants: The BBC, CNN, Fox News, Google News, The Guardian, The New York Post, The New York Times, The Wall Street Journal, The Washington Post, Yahoo News, and CBS News.
We also predicted that familiarity with our news sources would be positively associated with real news ratings and negatively associated with fake news ratings. To test this idea, we calculated-for each news source-correlations between familiarity and real news ratings, and familiarity and fake news ratings. In line with our prediction, we found that familiarity was positively associated with real news ratings across all news sources: maximum rGenuine(292) = 0.48, 95% CI [0.39, 0.57]; minimum rReal(292) = 0.15, 95% CI [0.04, 0.26]. But in contrast with what we predicted, we found that familiarity was also positively associated with fake news ratings, for two out of every three news sources: maximum rPhony(292) = 0.34, 95% CI [0.23, 0.44]; minimum rFake(292) = 0.12, 95% CI [0.01, 0.23]. Only one of the remaining 14 sources-CNN-was negatively correlated, rFake(292) = -0.15, 95% CI [-0.26, -0.03]; all other CIs crossed zero. Taken together, these exploratory results, while tentative, might suggest that familiarity with a news source leads to a bias in which people agree with any claim about that source.