Science eLetters
This short eLetter shows that our earlier published studies precede the recently claimed breakthrough in use of automated/software/AI-based deliberation to soften extremes of opinions.
Behind The Screen: How Bots Are Quietly Shaping The Digital World
Forbes
It began with just one post. Late one night, I was scrolling through my social media feed, and something caught my eye. You know that feeling when something doesn’t sit quite right, like an image in a puzzle that doesn’t fit.
Non-equilibrium physics of multi-species assembly applied to fibrils inhibition in biomolecular condensates and growth of online distrust
Scientific Reports
Self-assembly is a key process in living systems—from the microscopic biological level (e.g. assembly of proteins into fibrils within biomolecular condensates in a human cell) through to the macroscopic societal level (e.g. assembly of humans into common-interest communities across online social media platforms). The components in such systems (e.g. macromolecules, humans) are highly diverse, and so are the self-assembled structures that they form. However, there is no simple theory of how such structures assemble from a multi-species pool of components. Here we provide a very simple model which trades myriad chemical and human details for a transparent analysis, and yields results in good agreement with recent empirical data. It reveals a new inhibitory role for biomolecular condensates in the formation of dangerous amyloid fibrils, as well as a kinetic explanation of why so many diverse distrust movements are now emerging across social media. The nonlinear dependencies that we uncover suggest new real-world control strategies for such multi-species assembly.
Pedro Manrique, Frank Yingjie Huo, Sara El Oud, Neil Johnson
10 Predictions of Unknown Unknowns in Current and Future Conflicts
September 2024, DOD Basic Research Forum
Abstract: Human conflict will always appear chaotic and serve up unknown unknowns. But this talk shows how combining simple mechanistic representations of how fighters fight with state-of-the-art data, provides new insight into future unknown unknowns – and quantitative predictions. Examples include the Israel-Palestine region, Russia-Ukraine and also future ‘Total War’ scenarios in which cyber-terrorism and malicious AI use will play a role. The predictions range from future casualties to unexpected technological advancements by an adversary; new and unanticipated tactics such as cyberattacks or biological warfare; sudden alliances or geopolitical shifts that alter the balance of power; and insurgencies or terrorist attacks in areas or by groups not previously considered a threat. This empirically-grounded mechanistic perspective on future warfare, threats, and total surprises, can inform future interventions, hidden shifts and casualty risk.
Bad Actor AI and Defending the Online Battlefield with Prof Neil Johnson
Paradigm Podcast
Neil Johnson is a professor of physics at George Washington University. He heads up the Dynamic Online Networks Lab, which combines modern data science with cross-disciplinary fundamental research to tackle problems such as the spread of online misinformation, and the impact of bad-actor generative AI tools in online battlefields.
Neil is a Fellow of the American Physical Society (APS), was former Research Fellow at the University of Cambridge, and Professor of Physics at the University of Oxford. His published books include Financial Market Complexity, and Simply Complexity: A Clear Guide to Complexity Theory.
‘A lack of trust’: How deepfakes and AI could rattle the US elections
Al Jazeera
At least 20 states have passed regulations against election deepfakes, but federal action remains stalled.
How can physics help solve real world problems? – NEIL JOHNSON, Head of Dynamic Online Networks Lab
Education, The Creative Process Podcast
How can physics help solve messy, real world problems? How can we embrace the possibilities of AI while limiting existential risk and abuse by bad actors?
Neil Johnson is a physics professor at George Washington University. His new initiative in Complexity and Data Science at the Dynamic Online Networks Lab combines cross-disciplinary fundamental research with data science to attack complex real-world problems. His research interests lie in the broad area of Complex Systems and ‘many-body’ out-of-equilibrium systems of collections of objects, ranging from crowds of particles to crowds of people and from environments as distinct as quantum information processing in nanostructures to the online world of collective behavior on social media.
Online hate stems from small platforms, researchers find
GW Hatchet
Researchers found online hate develops on smaller social media platforms instead of mainstream ones in a study published earlier this month.
Nonlinear spreading behavior across multi-platform social media universe
Chaos: An Interdisciplinary Journal of Nonlinear Science
Understanding how harmful content (mis/disinformation, hate, etc.) manages to spread among online communities within and across social media platforms represents an urgent societal challenge. We develop a non-linear dynamical model for such viral spreading, which accounts for the fact that online communities dynamically interconnect across multiple social media platforms. Our mean-field theory (Effective Medium Theory) compares well to detailed numerical simulations and provides a specific analytic condition for the onset of outbreaks (i.e., system-wide spreading). Even if the infection rate is significantly lower than the recovery rate, it predicts system-wide spreading if online communities create links between them at high rates and the loss of such links (e.g., due to moderator pressure) is low. Policymakers should, therefore, account for these multi-community dynamics when shaping policies against system-wide spreading.
Shockwaves Link (Anti)Social Behavior Across the Internet
Springer-Nature “Behind the Paper”
A first-of-its kind network map of the online hate ecosystem provides new insight into decentralized behavior during January 6, 2021, and its implications for 2024 and beyond