Research Article

Fake news on Twitter during the 2016 U.S. presidential election

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Science  25 Jan 2019:
Vol. 363, Issue 6425, pp. 374-378
DOI: 10.1126/science.aau2706
  • Fig. 1 Prevalence over time and concentration of fake news sources.

    (A) Daily percentage of exposures to black, red, and orange fake news sources, relative to all exposures to political URLs. Exposures were summed across all panel members. (B to D) Empirical cumulative distribution functions showing distribution of exposures among websites (B), distribution of shares by panel members (C), and distribution of exposures among panel members (D). The x axis represents percentage of websites or panel members responsible for a given percentage (y axis) of all exposures or shares. Black, red, and orange lines represent fake news sources; blue line denotes all other sources. This distribution was not comparable for (B) because of the much larger number of sources in its tail and the fundamentally different selection process involved.

  • Fig. 2 Shares and exposures of political URLs by outlier accounts, many of which were also SS-F accounts.

    (A) Overall supersharers: top 1% among panelists sharing any political URLs, accounting for 49% of all shares and 82% of fake news shares. Letters above bars indicate political affinities. (B) Overall superconsumers: top 1% among panelists exposed to any political URLs, accounting for 12% of all exposures and 74% of fake news exposures. Black, red, and orange bars represent content from fake news sources; yellow or gray bars denote nonfake content (SS-F accounts are shown in yellow). The rightmost bar shows, for scale, the remainder of the panel’s fake news shares (A) or exposures (B).

  • Fig. 3 Probability density estimates for the percentage of content from fake news sources in people’s news feeds (for people with any fake news exposures).

    The number of individuals in each subgroup (N) and the percent with nonzero exposures to fake news sources are shown.

  • Fig. 4 Key individual characteristics associated with exposure to and sharing of fake news sources.

    The proportion of an individual’s political exposures coming from fake news sources as a function of (A) number of political exposures, excluding fake news sources, and (B) age. Estimates are based on binomial regression models fitted separately to each political affinity subgroup. Blue, liberal; black, center; red, conservative. (C to E) An individual’s likelihood of sharing one or more URLs from fake news sources as a function of (C) number of shares of political URLs, (D) number of exposures to fake news sources, and (E) political affinity. (F to I) Likelihood of a liberal (D) or conservative (R) individual sharing a political URL to which they have been exposed, depending on the political congruency and veracity of the source: (F) congruent and fake, (G) incongruent and fake, (H) congruent and nonfake, and (I) incongruent and nonfake. Brackets indicate significantly different pairs: **P < 0.01, ***P < 0.001. All estimates and 95% CIs [gray shaded regions in (A) to (D); line segments in (E) to (I)] are based on regression models specified in SM S.11 to S.13, with the remaining model variables held constant to their median or most common level.

  • Fig. 5 Coexposure network.

    Each node is a political news, blog, or fact-checking website. Edges link pairs of sites where an unusually high number of (nonoutlier) panel members were exposed to content from both sites, controlling for the popularity of each site. Filled nodes represent fake news sources. Node colors indicate groups (1, green; 2, orange; 3, purple; 4, gray) identified via an ensemble of clustering algorithms. Sites with the highest exposures are sized slightly larger. See fig. S10 for node labels.

Supplementary Materials

  • Fake news on Twitter during the 2016 U.S. presidential election

    Nir Grinberg, Kenneth Joseph, Lisa Friedland, Briony Swire-Thompson, David Lazer

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

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    • Supplementary Text 
    • Figs. S1 to S14
    • Tables S1 to S7
    • References 

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