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Prevalence and Propagation of Fake News

descriptionPublicationkeyboard_double_arrow_right Article , Other literature type , Preprint 19 Apr 2023Embargo end date: 01 Jan 2021 English Publisher:Informa UK LimitedJournal:Statistics and Public Policy, volume 10 (eissn: 2330-443X,

Authors: Behzad, Banafsheh; Bheem, Bhavana; Elizondo, Daniela; Marsh, Deyana; Martonosi, Susan;

doi: 10.1080/2330443x.2023.2190368 , 10.48550/arxiv.2106.09586 , 10.6084/m9.figshare.22280645 , 10.6084/m9.figshare.22280645.v1

arXiv: http://arxiv.org/abs/2106.09586

Prevalence and Propagation of Fake News

- Summary
- Subjects
- Metrics

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.

45 pages, 22 figures. Submitted for peer review on 7 May 2021

Related Organizations

Rutgers, The State University of New Jersey
United States
Harvey Mudd College
United States
Claremont Colleges
United States
California State University, Long Beach
United States

Keywords

Social and Information Networks (cs.SI), FOS: Computer and information sciences, J.4, Computer Science - Social and Information Networks, Policy recommendation, Statistics - Applications, QA273-280, Bias, Optimization and Control (math.OC), JF20-2112, 90B50, FOS: Mathematics, Social media analysis, Applications (stat.AP), Truthfulness, Political institutions and public administration (General), Probabilities. Mathematical statistics, Mathematics - Optimization and Control

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	3
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average