Controlling bad-actor-AI activity at scale across online battlefields

We show how the looming threat of bad actors using AI/GPT to generate harms across social media, can be addressed at scale by exploiting the intrinsic dynamics of the social media multiverse. We combine a uniquely detailed description of the current bad-actor-mainstream battlefield with a mathematical description of its behavior, to show what bad-actor-AI activity will likely dominate, where, and when. A dynamical Red Queen analysis predicts an escalation to daily bad-actor-AI activity by early 2024, just ahead of U.S. and other global elections. We provide a Policy Matrix that quantifies outcomes and trade-offs mathematically for the policy options of containment vs. removal. We give explicit plug-and-play formulae for risk measures

Rise of post-pandemic resilience across the distrust ecosystem

Why is distrust (e.g. of medical expertise) now flourishing online despite the surge in mitigation schemes being implemented? We analyze the changing discourse in the Facebook ecosystem of approximately 100 million users who pre-pandemic were focused on (dis)trust of vaccines. We find that post-pandemic, their discourse strongly entangles multiple non-vaccine topics and geographic scales both within and across communities. This gives the current distrust ecosystem a unique system-level resistance to mitigations that target a specific topic and geographic scale -- which is the case of many current schemes due to their funding focus, e.g. local health not national elections. Backed up by detailed numerical simulations, our results reveal the following counterintuitive solutions for implementing more effective mitigation schemes at scale: shift to 'glocal' messaging by (1) blending particular sets of distinct topics (e.g. combine messaging about specific diseases with climate change) and (2) blending geographic scales

Complexity of the Online Distrust Ecosystem and its Evolution

Collective human distrust (and its associated mis-disinformation) is one of the most complex phenomena of our time. e.g. distrust of medical expertise, or climate change science, or democratic election outcomes, and even distrust of fact-checked events in the current Israel-Hamas and Ukraine-Russia conflicts. So what makes the online distrust ecosystem so resilient? How has it evolved during and since the pandemic? And how well have Facebook mitigation policies worked during this time period? We analyze a Facebook network of interconnected in-built communities (Facebook pages) totaling roughly 100 million users who pre-pandemic were just focused on distrust of vaccines. Mapping out this dynamical network from 2019 to 2023, we show that it has quickly self-healed in the wake of Facebook's mitigation campaigns which include shutdowns. This confirms and extends our earlier finding that Facebook's ramp-ups during COVID were ineffective (e.g. November 2020). Our findings show that future interventions must be chosen to resonate across multiple topics and across multiple geographical scales. Unlike many recent studies, our findings do not rely on third-party black-box tools whose accuracy for rigorous scientific research is unproven, hence raising doubts about such studies' conclusions, nor is our network built using fleeting hyperlink mentions which have questionable relevance

Adaptive link dynamics drive online hate networks and their mainstream influence

Online hate is dynamic, adaptive -- and is now surging armed with AI/GPT tools. Its consequences include personal traumas, child sex abuse and violent mass attacks. Overcoming it will require knowing how it operates at scale. Here we present this missing science and show that it contradicts current thinking. Waves of adaptive links connect the hate user base over time across a sea of smaller platforms, allowing hate networks to steadily strengthen, bypass mitigations, and increase their direct influence on the massive neighboring mainstream. The data suggests 1 in 10 of the global population have recently been exposed, including children. We provide governing dynamical equations derived from first principles. A tipping-point condition predicts more frequent future surges in content transmission. Using the U.S. Capitol attack and a 2023 mass shooting as illustrations, we show our findings provide abiding insights and quantitative predictions down to the hourly scale. The expected impacts of proposed mitigations can now be reliably predicted for the first time

Shockwaves and turbulence across social media

Online communities featuring 'anti-X' hate and extremism, somehow thrive online despite moderator pressure. We present a first-principles theory of their dynamics, which accounts for the fact that the online population comprises diverse individuals and evolves in time. The resulting equation represents a novel generalization of nonlinear fluid physics and explains the observed behavior across scales. Its shockwave-like solutions explain how, why and when such activity rises from 'out-of-nowhere', and show how it can be delayed, re-shaped and even prevented by adjusting the online collective chemistry. This theory and findings should also be applicable to anti-X activity in next-generation ecosystems featuring blockchain platforms and Metaverses.Comment: Feedback welcome to [email protected]

SerpinE1 drives a cell-autonomous pathogenic signaling in Hutchinson-Gilford progeria syndrome

Hutchinson-Gilford progeria syndrome (HGPS) is a rare, fatal disease caused by Lamin A mutation, leading to altered nuclear architecture, loss of peripheral heterochromatin and deregulated gene expression. HGPS patients eventually die by coronary artery disease and cardiovascular alterations. Yet, how deregulated transcriptional networks at the cellular level impact on the systemic disease phenotype is currently unclear. A genome-wide analysis of gene expression in cultures of primary HGPS fibroblasts identified SerpinE1, also known as Plasminogen Activator Inhibitor (PAI-1), as central gene that propels a cell-autonomous pathogenic signaling from the altered nuclear lamina. Indeed, siRNA-mediated downregulation and pharmacological inhibition of SerpinE1 by TM5441 could revert key pathological features of HGPS in patient-derived fibroblasts, including re-activation of cell cycle progression, reduced DNA damage signaling, decreased expression of pro-fibrotic genes and recovery of mitochondrial defects. These effects were accompanied by the correction of nuclear abnormalities. These data point to SerpinE1 as a novel potential effector and target for therapeutic interventions in HGPS pathogenesis

Rise of post-pandemic resilience across the distrust ecosystem

Abstract Why does online distrust (e.g., of medical expertise) continue to grow despite numerous mitigation efforts? We analyzed changing discourse within a Facebook ecosystem of approximately 100 million users who were focused pre-pandemic on vaccine (dis)trust. Post-pandemic, their discourse interconnected multiple non-vaccine topics and geographic scales within and across communities. This interconnection confers a unique, system-level (i.e., at the scale of the full network) resistance to mitigations targeting isolated topics or geographic scales—an approach many schemes take due to constrained funding. For example, focusing on local health issues but not national elections. Backed by numerical simulations, we propose counterintuitive solutions for more effective, scalable mitigation: utilize “glocal” messaging by blending (1) strategic topic combinations (e.g., messaging about specific diseases with climate change) and (2) geographic scales (e.g., combining local and national focuses)

DataSheet1_Complexity of the online distrust ecosystem and its evolution.PDF

Introduction: Collective human distrust—and its associated mis/disinformation—is one of the most complex phenomena of our time, given that approximately 70% of the global population is now online. Current examples include distrust of medical expertise, climate change science, democratic election outcomes—and even distrust of fact-checked events in the current Israel-Hamas and Ukraine-Russia conflicts.Methods: Here we adopt the perspective of the system being a complex dynamical network, in order to address these questions. We analyze a Facebook network of interconnected in-built communities (Facebook Page communities) totaling roughly 100 million users who, prior to the pandemic, were just focused on distrust of vaccines.Results: Mapping out this dynamical network from 2019 to 2023, we show that it has quickly self-healed in the wake of Facebook’s mitigation campaigns which include shutdowns. This confirms and extends our earlier finding that Facebook’s ramp-ups during COVID-19 were ineffective (e.g., November 2020). We also show that the post-pandemic network has expanded its topics and has developed a dynamic interplay between global and local discourses across local and global geographic scales.Discussion: Hence current interventions that target specific topics and geographical scales will be ineffective. Instead, our findings show that future interventions need to resonate across multiple topics and across multiple geographical scales. Unlike many recent studies, our findings do not rely on third-party black-box tools whose accuracy for rigorous scientific research is unproven, hence raising doubts about such studies’ conclusions–nor is our network built using fleeting hyperlink mentions which have questionable relevance.</p