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The spread of true and false news online

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Science  09 Mar 2018:
Vol. 359, Issue 6380, pp. 1146-1151
DOI: 10.1126/science.aap9559
  • Fig. 1 Rumor cascades.

    (A) An example rumor cascade collected by our method as well as its depth, size, maximum breadth, and structural virality over time. “Nodes” are users. (B) The complementary cumulative distribution functions (CCDFs) of true, false, and mixed (partially true and partially false) cascades, measuring the fraction of rumors that exhibit a given number of cascades. (C) Quarterly counts of all true, false, and mixed rumor cascades that diffused on Twitter between 2006 and 2017, annotated with example rumors in each category. (D) The CCDFs of true, false, and mixed political cascades. (E) Quarterly counts of all true, false, and mixed political rumor cascades that diffused on Twitter between 2006 and 2017, annotated with example rumors in each category. (F) A histogram of the total number of rumor cascades in our data across the seven most frequent topical categories.

  • Fig. 2 Complementary cumulative distribution functions (CCDFs) of true and false rumor cascades.

    (A) Depth. (B) Size. (C) Maximum breadth. (D) Structural virality. (E and F) The number of minutes it takes for true and false rumor cascades to reach any (E) depth and (F) number of unique Twitter users. (G) The number of unique Twitter users reached at every depth and (H) the mean breadth of true and false rumor cascades at every depth. In (H), plot is lognormal. Standard errors were clustered at the rumor level (i.e., cascades belonging to the same rumor were clustered together; see supplementary materials for additional details).

  • Fig. 3 Complementary cumulative distribution functions (CCDFs) of false political and other types of rumor cascades.

    (A) Depth. (B) Size. (C) Maximum breadth. (D) Structural virality. (E and F) The number of minutes it takes for false political and other false news cascades to reach any (E) depth and (F) number of unique Twitter users. (G) The number of unique Twitter users reached at every depth and (H) the mean breadth of these false rumor cascades at every depth. In (H), plot is lognormal. Standard errors were clustered at the rumor level.

  • Fig. 4 Models estimating correlates of news diffusion, the novelty of true and false news, and the emotional content of replies to news.

    (A) Descriptive statistics on users who participated in true and false rumor cascades as well as K-S tests of the differences in the distributions of these measures across true and false rumor cascades. (B) Results of a logistic regression model estimating users’ likelihood of retweeting a rumor as a function of variables shown at the left. coeff, logit coefficient; z, z score. (C) Differences in the information uniqueness (IU), scaled Bhattacharyya distance (BD), and K-L divergence (KL) of true (green) and false (red) rumor tweets compared to the corpus of prior tweets the user was exposed to in the 60 days before retweeting the rumor tweet. (D) The emotional content of replies to true (green) and false (red) rumor tweets across seven dimensions categorized by the NRC. (E) Mean and variance of the IU, KL, and BD of true and false rumor tweets compared to the corpus of prior tweets the user has seen in the 60 days before seeing the rumor tweet as well as K-S tests of their differences across true and false rumors. (F) Mean and variance of the emotional content of replies to true and false rumor tweets across seven dimensions categorized by the NRC as well as K-S tests of their differences across true and false rumors. All standard errors are clustered at the rumor level, and all models are estimated with cluster-robust standard errors at the rumor level.

Supplementary Materials

  • The spread of true and false news online

    Soroush Vosoughi, Deb Roy, Sinan Aral

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

    Download Supplement
    • Materials and Methods
    • Figs. S1 to S20
    • Tables S1 to S39
    • References

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