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. 2020 Nov 10;11:5764. doi: 10.1038/s41467-020-19644-6

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 7 — Top row of panels shows the results of the expanded analysis using two independent OLS models for the placebo keywords “economy” (panel (a)); “football” (b); “gardening” (c); “skiiing” (d); and “vegetarian” (e). 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. The word clouds accompanying each plot represent the 50 most frequent words found in the NYT articles selected on the basis of the corresponding keyword.