El Pais
Several investigations identify the brain mechanisms that make us share hoaxes and a “vaccine” to prevent them
Losing the battle over best-science guidance early in a crisis: COVID-19 and beyond
Science Advances
Ensuring widespread public exposure to best-science guidance is crucial in any crisis, e.g., coronavirus disease 2019 (COVID-19), monkeypox, abortion misinformation, climate change, and beyond. We show how this battle got lost on Facebook very early during the COVID-19 pandemic and why the mainstream majority, including many parenting communities, had already moved closer to more extreme communities by the time vaccines arrived. Hidden heterogeneities in terms of who was talking and listening to whom explain why Facebook’s own promotion of best-science guidance also appears to have missed key audience segments. A simple mathematical model reproduces the exposure dynamics at the system level. Our findings could be used to tailor guidance at scale while accounting for individual diversity and to help predict tipping point behavior and system-level responses to interventions in future crises.
Lucia Illari, Nicholas J. Restrepo, Neil F. Johnson
Losing the Battle for Hearts and Minds Early in a Crisis
Newswise
New study reveals how mainstream Facebook communities were already heavily intertwined with groups opposing best-science guidance long before COVID-19 vaccines arrived.
Turning a losing battle into a winning one: Navigating and Countering the Minefield of Misinformation on Social Media
Keynote discussion panel at International Conference on Emerging Infectious Diseases (ICEID 2022) August 2022, Atlanta, GA: https://www.iceid.org/
Dynamic Topic Modeling Reveals Variations in Online Hate Narratives
Intelligent Computing
Online hate speech can precipitate and also follow real-world violence, such as the U.S. Capitol attack on January 6, 2021. However, the current volume of content and the wide variety of extremist narratives raise major challenges for social media companies in terms of tracking and mitigating the activity of hate groups and broader extremist movements. This is further complicated by the fact that hate groups and extremists can leverage multiple platforms in tandem in order to adapt and circumvent content moderation within any given platform (e.g. Facebook). We show how the computational approach of dynamic Latent Dirichlet Allocation (LDA) may be applied to analyze similarities and differences between online content that is shared across social media platforms by extremist communities, including Facebook, Gab, Telegram, and VK between January and April 2021. We also discuss characteristics revealed by unsupervised machine learning about how hate groups leverage sites to organize, recruit, and coordinate within and across such online platforms.
Richard Sear, Nicholas Johnson Restrepo, Yonatan Lupu, Neil F. Johnson
The Multiverse of Online Extremism is Held Together by Hate’s Gravity
Exploring Hate (Brookings)
Rhys Leahy, Nicolas Velásquez, Nicholas Johnson Restrepo, Yonatan Lupu, Beth Goldberg, Neil F. Johnson
Connectivity Between Russian Information Sources and Extremist Communities Across Social Media Platforms
Frontiers in Political Science
The current military conflict between Russia and Ukraine is accompanied by disinformation and propaganda within the digital ecosystem of social media platforms and online news sources. One month prior to the conflict’s February 2022 start, a Special Report by the U.S. Department of State had already highlighted concern about the extent to which Kremlin-funded media were feeding the online disinformation and propaganda ecosystem. Here we address a closely related issue: how Russian information sources feed into online extremist communities. Specifically, we present a preliminary study of how the sector of the online ecosystem involving extremist communities interconnects within and across social media platforms, and how it connects into such official information sources. Our focus here is on Russian domains, European Nationalists, and American White Supremacists. Though necessarily very limited in scope, our study goes beyond many existing works that focus on Twitter, by instead considering platforms such as VKontakte, Telegram, and Gab. Our findings can help shed light on the scope and impact of state-sponsored foreign influence operations. Our study also highlights the need to develop a detailed map of the full multi-platform ecosystem in order to better inform discussions aimed at countering violent extremism.
Rhys Leahy, Nicholas Johnson Restrepo, Richard Sear, Neil F. Johnson
Russian media drives online hate
Axios
There is a clear and growing link between Russian propaganda and online far-right extremism globally, according to a new study from researchers at the George Washington University.
Science of online ‘anti-X’ and a scalable solution: From vaccine misinformation to extremism
Keynote talk at the International Workshop on Cyber Social Threats (CySoc) June 2022, Atlanta, GA: https://cysoc2022.github.io/
Using Neural Architectures to Model Complex Dynamical Systems
Advances in Artificial Intelligence and Machine Learning
The natural, physical and social worlds abound with feedback processes that make the challenge of modeling the underlying system an extremely complex one. This paper proposes an end-to-end deep learning approach to modelling such so-called complex systems which addresses two problems: (1) scientific model discovery when we have only incomplete/partial knowledge of system dynamics; (2) integration of graph-structured data into scientific machine learning (SciML) using graph neural networks. It is well known that deep learning (DL) has had remarkable success in leveraging large amounts of unstructured data into downstream tasks such as clustering, classification, and regression. Recently, the development of graph neural networks has extended DL techniques to graph structured data of complex systems. However, DL methods still appear largely disjointed with established scientific knowledge, and the contribution to basic science is not always apparent. This disconnect has spurred the development of physics-informed deep learning, and more generally, the emerging discipline of SciML. Modelling complex systems in the physical, biological, and social sciences within the SciML framework requires further considerations. We argue the need to consider heterogeneous, graph-structured data as well as the effective scale at which we can observe system dynamics. Our proposal would open up a joint approach to the previously distinct fields of graph representation learning and SciML.
Nicholas Gabriel, Neil F. Johnson