Fig. 6: The association between Brexit coverage in the NYT and diversion, and the association between diversion and subsequent Brexit coverage.
From: Using the president’s tweets to understand political diversion in the age of social media
For this analysis, only NYT articles that did not mention Russia or Mueller were considered (N = 101, 435). Panel (a) is for two independent OLS models, panel (b) is a single 3SLS model in which suppression is modeled by relating yesterday’s tweets to today’s coverage, and c is a 3SLS model in which suppression is modeled by relating today’s tweets to tomorrow’s coverage. In each panel, the axes show jittered t-values of the regression coefficients for diversion (X-axis) and suppression (Y-axis). Each point represents diversion and suppression for one pair of words in the Twitter vocabulary. Red vertical and horizontal lines denote significance thresholds (±1.96). Word pairs that are triggered by coverage of the corresponding keyword (p < 0.05) and affect subsequent coverage (p < 0.05) are plotted in red. The gray contour lines in each panel show the distribution of points obtained if the timeline of tweets is randomized (red perimeter represents 95% cutoff, see “Methods”). The blue rugs represent univariate distributions.
