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Current events have brought the spread of false information via social media to the forefront of national consciousness. With Mark Zuckerberg’s ongoing testimony on Capitol Hill regarding Facebook’s role in the proliferation of fake news giving us insight into what happened, it is important to identify the how, i.e., the mechanism by which misinformation goes viral. Using machine learning, complex network theory, and methods such as Granger causality on a massive Twitter dataset numbering 171 million relevant Tweets, members of our lab uncovered patterns in how various sources of fake news influenced– and were influenced by– their followers.

We find, for example, that unlike the influential spreaders of traditional news, which are largely the Twitter accounts of verified sources such as the New York Times or CNN, the influencers in the fake news dataset are largely users whose accounts were not verified or have since been deleted. Moreover, while the opinions of users identified as being more left-leaning seemed to be influenced by traditional centrist or left-leaning media sources’ coverage of the Democratic candidate Hillary Rodham Clinton, the opinions of users identified to be more right-leaning were found to actually have an influence on the fake news influencers.

CCNY physicist tracks influence of fake news on Presidential election CCNY News (2018), Rebecca Rivera

Influence of fake news in Twitter during the 2016 US Presidential election arXiv (2018), Alexandre Bovet and Hernán A. Makse

 

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April 12th, 2018

CCNY physicist tracks influence of fake news on Presidential election

    Current events have brought the spread of false information via social media to the forefront of national consciousness. […]

February 5th, 2017

Science: Predictions: The Pulse of the People

Volume 355 of Science (February 03, 2017) focuses on the use of scientific methods to predict population-level trends. The work […]

November 27th, 2016

Predicting election trends with Twitter: Hillary Clinton versus Donald Trump.

The recently-concluded United States Presidential election was the end to one of the most divisive and vitriolic campaign seasons in […]

August 6th, 2015

Nature: Optimal Percolation: Destruction perfected

NATURE | NEWS & VIEWS Nature 524, 38–39 (06 August 2015) In complex networks, some nodes are more important than others. […]

October 6th, 2014

Nature Physics: Brain Network of Networks: Why natural networks are more stable than man-made networks

Connecting complex networks is known to exacerbate perturbations and lead to cascading failures, but natural networks of networks like the […]

May 6th, 2014

MIT Tech Review: The Emerging Science of Superspreaders (And How to Tell If You’re One Of Them)

From MIT Technology Review. Nobody has figured out how to spot the most influential spreaders of information in a real-world […]

February 6th, 2014

Scientific Reports: Large cities are less green but help to reduce suicidal rates

Work done in collaboration with Jose S. Andrade from Universidade Federal de Ceara, Brazil. Large cities are more productive than […]

February 6th, 2014

Soft Matter: Fundamental challenges in packing problems: from spherical to non-spherical particles

by Adrian Baule and Hernan Makse. Random packings of objects of a particular shape are ubiquitous in science and engineering. […]

July 6th, 2013

Nature Communications: Mean-field theory of random close packings of axisymmetric particles

by Adrian Baule, Romain Mari, Lin Bo, Louis Portal, Hernan A. Makse. Finding the densest random packing of particles with […]

March 21st, 2013

PLoS ONE: Novel insights into the evolution of protein networks

March 21, 2013. Paper in PLOS ONE. System-wide networks of proteins are indispensable for organisms. Function and evolution of these […]