Prevalence and Propagation of Fake News

ABSTRACT

In recent years, scholars have raised concerns on the effects that unreliable news, or “fake news,” has on our political sphere, and our democracy as a whole. For example, the propagation of fake news on social media is widely believed to have influenced the outcome of national elections, including the 2016 U.S. Presidential Election, and the 2020 COVID-19 pandemic. What drives the propagation of fake news on an individual level, and which interventions could effectively reduce the propagation rate? Our model disentangles bias from truthfulness of an article and examines the relationship between these two parameters and a reader’s own beliefs. Using the model, we create policy recommendations for both social media platforms and individual social media users to reduce the spread of untruthful or highly biased news. We recommend that platforms sponsor unbiased truthful news, focus fact-checking efforts on mild to moderately biased news, recommend friend suggestions across the political spectrum, and provide users with reports about the political alignment of their feed. We recommend that individual social media users fact check news that strongly aligns with their political belief and read articles of opposing political bias. Supplementary materials for this article are available online.

KEYWORDS:

• Bias
• Policy recommendation
• Social media analysis
• Truthfulness

Supplementary Materials

The supplementary materials include (1) level curves of the probability model; (2) comparison of model parameters between typical and atypical users; (3) sensitivity of population results to estimated model parameters; and (4) proof that population diversity reduces sharing rates.

Acknowledgments

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors would like to thank Michael Gao and Steven Witkin for their contributions to earlier versions of this work, and Deyana Marsh for her substantial contributions to the computational testing. Additionally, the authors thank David Lazer and Nir Grinberg for providing access to the dataset from (Grinberg et al. 2019). Finally, the authors thank the anonymous reviewers for detailed feedback that greatly improved this manuscript.

Disclosure Statement

The authors report there are no competing interests to declare.

Notes

1 Readers may not always be able to distinguish truth from untruth with high precision. However, we are modeling the likelihood of sharing an article. External factors such as the reputability of the source can serve as signals to readers as to the truthfulness of content.

2 A summary of all recommendations made in this article can be found in Table 5 in Section 6

3 The Grinberg et al. data includes alignment scores, the weighted average of panelists exposed to a website that are registered with either the Democratic or Republican party, for only 245 distinct websites.

4 This result can be supported mathematically, as shown in SM Section 4.

Additional information

Funding

This material is based upon work supported by the National Science Foundation under grant no. DMS-1757952. The authors also acknowledge financial support from Harvey Mudd College and the California State University at Long Beach.