Influence of fake news in Twitter during the 2016 US presidential election

By A. Bovet, Hernán A. Makse. Nature Communications, 2018

817 citations recorded by the source corpus.

The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources.co, we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders. The influence of 'fake news’, spread via social media, has been much discussed in the context of the 2016 US presidential election. Here, the authors use data on 30 million tweets to show how content classified as fake news diffused on Twitter before the election.

Subfields: Communication, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/e6914176dc466e7db55a23d7f3ded9bd1ac9d0a0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

In Information Systems

In Sociology and Political Science

Citations in the corpus

Growing polarization around climate change on social media (2021, background): “However, many studies highlight the importance of Twitter as a critical tool for studying climate communication [10, 11], political polarisation [12, 13] , and misinformation [14].”
Fake news, disinformation and misinformation in social media: a review (2023, background): “…of online news on Twitter (Vosoughi et al. 2018) that the spread of false news online is six times faster than truthful content and that 70% of the users could not distinguish real from fake news (Vosoughi et al. 2018) due to the attraction of the novelty of the latter (Bovet and Makse 2019).”
Fake News Detection on Social Media using Geometric Deep Learning (2019, background): “The extensive spread of fake news before the United States 2016 presidential elections [3] and the Brexit vote in United Kingdom has become the centerpiece of the controversy surrounding these political events and allegations of public opinion manipulation.”
Preserving integrity in online social networks (2020, background): “Bovet and Makse [20] showed that social bots likely played a major role in spreading political misinformation on Twitter during the 2016 election in the United States.”
An exploratory study of COVID-19 misinformation on Twitter (2020, neutral): “Although such source-based approaches are popular and allow for a large-scale analysis of Twitter data [6, 99, 10, 30, 72], the reliability of news sources remains a subject of considerable disagreement [90, 60].”
The COVID-19 social media infodemic (2020, background): “While most of the studies on misinformation diffusion focus on a single platform [22, 24, 14], the dynamics behind information consumption might be particular to the environment in which they spread on.”
All the News That’s Fit to Click: The Economics of Clickbait Media (2019, neutral): “Bovet and Makse (2019) use this to their advantage to track the networks through which mainstream and zero-credibility news spread during the election.”
The Reception of Fake News: The Interpretations and Practices That Shape the Consumption of Perceived Misinformation (2019, background): “They suggested that the effects might not have been large, as its diffusion occurred in tightly connected clusters that did not necessarily influence the overall social media sphere (Allcott and Gentzkow 2017; Bovet and Makse 2018; Grinberg et al. 2019; Guess et al. 2018; Nelson and Taneja 2018).”