Diffusion in networks: fake news and random walks
We combined machine learning, network science, statistical physics and causality analysis to measure the importance of fake news compared to traditional news in Twitter during the 2016 US presidential election and also to understand their influence and the mechanisms of their diffusion.
Using a dataset of more than 170 million tweets covering the five months preceding election day and concerning the two main candidates of the 2016 US presidential election, we find that 25% of the tweets linking to a news spread either fake or extremely biased news. We analyzed the networks of information flow and found the most important news spreaders by using the theory of optimal percolation and used a multivariate causal network reconstruction to uncover how fake news influenced Twitter activity during the presidential election.
Many social, biological or economic systems can be described as complex networks representing the interactions between multiple agents.
One important task to understand these systems is the problem of community detection, i.e. finding a simplified view of a complex systems' components and how they interact.
Community detection has been used productively in many applications, including identifying allegiances or personal interests in social networks, biological function in metabolic networks, the modular organization of the brain or fraud in financial transaction networks.
Here, we show that by modeling random walks on networks we can build a principled framework to describe and detect communities in complex networks and how this approach can be extended to the case of temporal networks to provide a principled method to study communities in non-stationary temporal networks.
Alexandre Bovet is a researcher at the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM) of the Université Catholique de Louvain working on complex systems and in their modelling using complex networks. He is interested in interdisciplinary approaches to answer biological, economic and social questions using tools from physics and data science.
He obtained his PhD in physics in 2015 from EPFL in Lausanne, Switzerland, for his work on the transport of particles in turbulent plasmas. He then moved to the Theoretical Biology lab at the ETHZ, Zurich, to investigate the spread of diseases in a contact network of a population of wild mice as well as its social and behavioral organization. In 2016, he moved to the Levich Institute of the City College of New York, USA, with a fellowship of the SNSF to work on the diffusion of information and opinion dynamics in social networks.