Category: Publications

SIAM News

Tragic acts of terrorism—such as February’s mass stabbing in Austria by a 23-year-old Syrian asylum seeker who was allegedly radicalized online by the Islamic State —accentuate the dangers of radicalization via the internet. Terrorist organizations exploit popular social media platforms to advance their ideology-driven agendas through recruitment, fundraising, and the spread of propaganda — all of which ultimately causes severe harm in communities around the world. From a national security perspective, this drive towards radicalization raises pressing questions about our ability to monitor, quantify, understand, predict, and even mitigate such efforts before they materialize as tragedies.

Pedro Manrique and Neil Johnson

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Europhysics Letters

Why humans fight has no easy answer. However, understanding better how humans fight could inform future humanitarian aid planning and insight into hidden shifts for peace efforts. Here we show that an empirically-grounded physics theory of fighter dynamics — which is a generalization of the well-known physics of polymer assembly — can explain casualty patterns observed across decades of violence in a current conflict hotspot. It also suggests the possibility of future ‘super-shock’ surprise attacks that are even more lethal than have already been seen. These insights from physics open the door to new policy discussions surrounding humanitarian aid and peace efforts that account mechanistically for human violence across scales.

Frank Yingjie Huo, Dylan Restrepo, Pedro Manrique, Gordon Woo, Neil Johnson

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NPJ Complexity

In early 2021, groups of Reddit and Twitter users collaborated to raise the price of GameStop stock from $20 to $400 in a matter of days. The heavy influence of social media activity on the rise of GameStop prices can be contrasted with the muted social media influence on other, more traditional stocks. While traditional stocks are modeled quite successfully by current methods, such methods break down when used to model these so-called meme stocks. Our project analyzes the graph topology of retweet graphs built from GameStop-related tweets and other meme stocks to find that the clustering coefficient and network diameter of a retweet graph can be used to decrease the mean absolute error of meme stock trading volume predictions by as much as 46% over the control group during the first 70 trading days of 2021.

Daniel Verdear, Neil Johnson, Stefan Wuchty

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NPJ Complexity

Politicization of the COVID-19 vaccination debate has lead to a polarization of opinions regarding this topic. We present a theoretical model of this debate on Facebook. In this model, agents form opinions through information that they receive from other agents with flexible opinions and from politically motivated entities such as media or interest groups. The model captures the co-evolution of opinions and network structure under similarity-dependent social influence, as well as random network re-wiring and opinion change. We show that attitudinal polarization can be avoided if agents (1) connect to agents all across the opinion spectrum, (2) receive information from many sources before changing their opinions, (3) frequently change opinions at random, and (4) frequently connect to friends of friends. High Kleinberg authority scores among politically motivated media and two network components that are comparable in size can indicate the onset of attitudinal polarization.

Mikhail Lipatov, Lucia Illari, Neil Johnson, Sergey Gavrilets

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Physical Review Letters

The global chaos caused by the July 19, 2024 technology meltdown highlights the need for a theory of what large-scale cohesive behaviors—dangerous or desirable—could suddenly emerge from future systems of interacting humans, machinery, and software, including artificial intelligence; when they will emerge; and how they will evolve and be controlled. Here, we offer answers by introducing an aggregation model that accounts for the interacting entities’ inter- and intraspecies diversities. It yields a novel multidimensional generalization of existing aggregation physics. We derive exact analytic solutions for the time to cohesion and growth of cohesion for two species, and some generalizations for an arbitrary number of species. These solutions reproduce—and offer a microscopic explanation for—an anomalous nonlinear growth feature observed in various current real-world systems. Our theory suggests good and bad “surprises” will appear sooner and more strongly as humans, machinery, artificial intelligence, and so on interact more, but it also offers a rigorous approach for understanding and controlling this.

Frank Yingjie Huo, Pedro Manrique, Neil Johnson

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NPJ Complexity

Local or national politics can be a catalyst for potentially dangerous hate speech. But with a third of the world’s population eligible to vote in 2024 elections, we need an understanding of how individual-level hate multiplies up to the collective global scale. We show, based on the most recent U.S. presidential election, that offline events are associated with rapid adaptations of the global online hate universe that strengthens both its network-of-networks structure and the types of hate content that it collectively produces. Approximately 50 million accounts in hate communities are drawn closer to each other and to a broad mainstream of billions. The election triggered new hate content at scale around immigration, ethnicity, and antisemitism that aligns with conspiracy theories about Jewish-led replacement. Telegram acts as a key hardening agent; yet, it is overlooked by U.S. Congressional hearings and new E.U. legislation. Because the hate universe has remained robust since 2020, anti-hate messaging surrounding global events (e.g., upcoming elections or the war in Gaza) should pivot to blending multiple hate types while targeting previously untouched social media structures.

Akshay Verma, Richard Sear, Neil Johnson

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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.

Neil Johnson

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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

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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.

Chenkai Xia, Neil Johnson

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NPJ Complexity

Online hate is dynamic, adaptive— and may soon surge with new AI/GPT tools. Establishing how hate operates at scale is key to overcoming it. We provide insights that challenge existing policies. Rather than large social media platforms being the key drivers, waves of adaptive links across smaller platforms connect the hate user base over time, fortifying hate networks, bypassing mitigations, and extending their direct influence into the massive neighboring mainstream. Data indicates that hundreds of thousands of people globally, including children, have been exposed. We present governing equations derived from first principles and a tipping-point condition predicting future surges in content transmission. Using the U.S. Capitol attack and a 2023 mass shooting as case studies, our findings offer actionable insights and quantitative predictions down to the hourly scale. The efficacy of proposed mitigations can now be predicted using these equations.

Minzhang Zheng, Richard Sear, Lucia Illari, Nicholas Restrepo, Neil Johnson

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