Prevalence and Propagation of Fake News

TL;DR

This paper models the spread of fake news on social media, analyzing how bias and truthfulness influence propagation, and offers policy recommendations for platforms and users to mitigate misinformation.

Contribution

It introduces a novel model disentangling bias from truthfulness and provides targeted strategies for reducing fake news dissemination.

Findings

Bias and truthfulness affect propagation dynamics

Platforms should promote unbiased and truthful content

Users should fact-check and explore opposing viewpoints

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 bias and read articles of opposing political bias.