The online competition between pro- and anti-vaccination views.

Distrust in scientific expertise1-14 is dangerous. Opposition to vaccination with a future vaccine against SARS-CoV-2, the causal agent of COVID-19, for example, could amplify outbreaks2-4, as happened for measles in 20195,6. Homemade remedies7,8 and falsehoods are being shared widely on the Internet, as well as dismissals of expert advice9-11. There is a lack of understanding about how this distrust evolves at the system level13,14. Here we provide a map of the contention surrounding vaccines that has emerged from the global pool of around three billion Facebook users. Its core reveals a multi-sided landscape of unprecedented intricacy that involves nearly 100 million individuals partitioned into highly dynamic, interconnected clusters across cities, countries, continents and languages. Although smaller in overall size, anti-vaccination clusters manage to become highly entangled with undecided clusters in the main online network, whereas pro-vaccination clusters are more peripheral. Our theoretical framework reproduces the recent explosive growth in anti-vaccination views, and predicts that these views will dominate in a decade. Insights provided by this framework can inform new policies and approaches to interrupt this shift to negative views. Our results challenge the conventional thinking about undecided individuals in issues of contention surrounding health, shed light on other issues of contention such as climate change11, and highlight the key role of network cluster dynamics in multi-species ecologies15.

Nonlinear spreading behavior across multi-platform social media universe.

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.

Emergence of Polarization in Coevolving Networks.

Polarization is a ubiquitous phenomenon in social systems. Empirical studies document substantial evidence for opinion polarization across social media, showing a typical bipolarized pattern devising individuals into two groups with opposite opinions. While coevolving network models have been proposed to understand polarization, existing works cannot generate a stable bipolarized structure. Moreover, a quantitative and comprehensive theoretical framework capturing generic mechanisms governing polarization remains unaddressed. In this Letter, we discover a universal scaling law for opinion distributions, characterized by a set of scaling exponents. These exponents classify social systems into bipolarized and depolarized phases. We find two generic mechanisms governing the polarization dynamics and propose a coevolving framework that counts for opinion dynamics and network evolution simultaneously. Under a few generic assumptions on social interactions, we find a stable bipolarized community structure emerges naturally from the coevolving dynamics. Our theory analytically predicts two-phase transitions across three different polarization phases in line with the empirical observations for the Facebook and blogosphere data sets. Our theory not only accounts for the empirically observed scaling laws but also allows us to predict scaling exponents quantitatively.

Shockwavelike Behavior 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 shockwavelike solutions explain how, why, and when such activity rises from "out-of-nowhere," and show how it can be delayed, reshaped, 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.

Objectively measured teacher and preschooler vocalizations: Phonemic diversity is associated with language abilities.

Over half of US children are enrolled in preschools, where the quantity and quality of language input from teachers are likely to affect children's language development. Leveraging repeated objective measurements, we examined the rate per minute and phonemic diversity of child and teacher speech-related vocalizations in preschool classrooms and their association with children's end-of-year receptive and expressive language abilities measured with the Preschool Language Scales (PLS-5). Phonemic diversity was computed as the number of unique consonants and vowels in a speech-related vocalization. We observed three successive cohorts of 2.5-3.5-year-old children enrolled in an oral language classroom that included children with and without hearing loss (N = 29, 16 girls, 14 Hispanic). Vocalization data were collected using child-worn audio recorders over 34 observations spanning three successive school years, yielding 21.53 mean hours of audio recording per child. The rate of teacher vocalizations positively predicted the rate of children's speech-related vocalizations while the phonemic diversity of teacher vocalizations positively predicted the phonemic diversity of children's speech-related vocalizations. The phonemic diversity of children's speech-related vocalizations was a stronger predictor of end-of-year language abilities than the rate of children's speech-related vocalizations. Mediation analyses indicated that the phonemic diversity of teacher vocalizations was associated with children's receptive and expressive language abilities to the extent that it influenced the phonemic diversity of children's own speech-related vocalizations. The results suggest that qualitatively richer language input expands the phonemic diversity of children's speech, which in turn is associated with language abilities.

Facebook Pages, the "Disneyland" Measles Outbreak, and Promotion of Vaccine Refusal as a Civil Right, 2009-2019.

Objectives. To understand changes in how Facebook pages frame vaccine opposition.Methods. We categorized 204 Facebook pages expressing vaccine opposition, extracting public posts through November 20, 2019. We analyzed posts from October 2009 through October 2019 to examine if pages' content was coalescing.Results. Activity in pages promoting vaccine choice as a civil liberty increased in January 2015, April 2016, and January 2019 (t[76] = 11.33 [P < .001]; t[46] = 7.88 [P < .001]; and t[41] = 17.27 [P < .001], respectively). The 2019 increase was strongest in pages mentioning US states (t[41] = 19.06; P < .001). Discussion about vaccine safety decreased (rs[119] = -0.61; P < .001) while discussion about civil liberties increased (rs[119] = 0.33; Py < .001]). Page categories increasingly resembled one another (civil liberties: rs[119] = -0.50 [P < .001]; alternative medicine: rs[84] = -0.77 [P < .001]; conspiracy theories: rs[119] = -0.46 [P < .001]; morality: rs[106] = -0.65 [P < .001]; safety and efficacy: rs[119] = -0.46 [P < .001]).Conclusions. The "Disneyland" measles outbreak drew vaccine opposition into the political mainstream, followed by promotional campaigns conducted in pages framing vaccine refusal as a civil right. Political mobilization in state-focused pages followed in 2019.Public Health Implications. Policymakers should expect increasing attempts to alter state legislation associated with vaccine exemptions, potentially accompanied by fiercer lobbying from specific celebrities.

Generalized Gelation Theory Describes Onset of Online Extremist Support.

We introduce a generalized form of gelation theory that incorporates individual heterogeneity and show that it can explain the asynchronous, sudden appearance and growth of online extremist groups supporting ISIS (so-called Islamic State) that emerged globally post-2014. The theory predicts how heterogeneity impacts their onset times and growth profiles and suggests that online extremist groups present a broad distribution of heterogeneity-dependent aggregation mechanisms centered around homophily. The good agreement between the theory and empirical data suggests that existing strategies aiming to defeat online extremism under the assumption that it is driven by a few "bad apples" are misguided. More generally, this generalized theory should apply to a range of real-world systems featuring aggregation among heterogeneous objects.

Common ecology quantifies human insurgency.

Many collective human activities, including violence, have been shown to exhibit universal patterns. The size distributions of casualties both in whole wars from 1816 to 1980 and terrorist attacks have separately been shown to follow approximate power-law distributions. However, the possibility of universal patterns ranging across wars in the size distribution or timing of within-conflict events has barely been explored. Here we show that the sizes and timing of violent events within different insurgent conflicts exhibit remarkable similarities. We propose a unified model of human insurgency that reproduces these commonalities, and explains conflict-specific variations quantitatively in terms of underlying rules of engagement. Our model treats each insurgent population as an ecology of dynamically evolving, self-organized groups following common decision-making processes. Our model is consistent with several recent hypotheses about modern insurgency, is robust to many generalizations, and establishes a quantitative connection between human insurgency, global terrorism and ecology. Its similarity to financial market models provides a surprising link between violent and non-violent forms of human behaviour.

Controlling bad-actor-artificial intelligence activity at scale across online battlefields.

We consider the looming threat of bad actors using artificial intelligence (AI)/Generative Pretrained Transformer to generate harms across social media globally. Guided by our detailed mapping of the online multiplatform battlefield, we offer answers to the key questions of what bad-actor-AI activity will likely dominate, where, when-and what might be done to control it at scale. Applying a dynamical Red Queen analysis from prior studies of cyber and automated algorithm attacks, predicts an escalation to daily bad-actor-AI activity by mid-2024-just ahead of United States and other global elections. We then use an exactly solvable mathematical model of the observed bad-actor community clustering dynamics, to build a Policy Matrix which quantifies the outcomes and trade-offs between two potentially desirable outcomes: containment of future bad-actor-AI activity vs. its complete removal. We also give explicit plug-and-play formulae for associated risk measures.

Rise of post-pandemic resilience across the distrust ecosystem.

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).

Inductive detection of influence operations via graph learning.

Influence operations are large-scale efforts to manipulate public opinion. The rapid detection and disruption of these operations is critical for healthy public discourse. Emergent AI technologies may enable novel operations that evade detection and influence public discourse on social media with greater scale, reach, and specificity. New methods of detection with inductive learning capacity will be needed to identify novel operations before they indelibly alter public opinion and events. To this end, we develop an inductive learning framework that: (1) determines content- and graph-based indicators that are not specific to any operation; (2) uses graph learning to encode abstract signatures of coordinated manipulation; and (3) evaluates generalization capacity by training and testing models across operations originating from Russia, China, and Iran. We find that this framework enables strong cross-operation generalization while also revealing salient indicators-illustrating a generic approach which directly complements transductive methodologies, thereby enhancing detection coverage.

Offline events and online hate.

Online hate speech is a critical and worsening problem, with extremists using social media platforms to radicalize recruits and coordinate offline violent events. While much progress has been made in analyzing online hate speech, no study to date has classified multiple types of hate speech across both mainstream and fringe platforms. We conduct a supervised machine learning analysis of 7 types of online hate speech on 6 interconnected online platforms. We find that offline trigger events, such as protests and elections, are often followed by increases in types of online hate speech that bear seemingly little connection to the underlying event. This occurs on both mainstream and fringe platforms, despite moderation efforts, raising new research questions about the relationship between offline events and online speech, as well as implications for online content moderation.

Energy transfer in N-component nanosystems enhanced by pulse-driven vibronic many-body entanglement.

The processing of energy by transfer and redistribution, plays a key role in the evolution of dynamical systems. At the ultrasmall and ultrafast scale of nanosystems, quantum coherence could in principle also play a role and has been reported in many pulse-driven nanosystems (e.g. quantum dots and even the microscopic Light-Harvesting Complex II (LHC-II) aggregate). Typical theoretical analyses cannot easily be scaled to describe these general N-component nanosystems; they do not treat the pulse dynamically; and they approximate memory effects. Here our aim is to shed light on what new physics might arise beyond these approximations. We adopt a purposely minimal model such that the time-dependence of the pulse is included explicitly in the Hamiltonian. This simple model generates complex dynamics: specifically, pulses of intermediate duration generate highly entangled vibronic (i.e. electronic-vibrational) states that spread multiple excitons - and hence energy - maximally within the system. Subsequent pulses can then act on such entangled states to efficiently channel subsequent energy capture. The underlying pulse-generated vibronic entanglement increases in strength and robustness as N increases.

Losing the battle over best-science guidance early in a crisis: COVID-19 and beyond.

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.

Inhalation Toxicity of Talc.

Respirable talc powder (RTP) is a complex mineral mixture of talc along with accessory minerals, including tremolite, anthophyllite, quartz, magnesite, dolomite, antigorite, lizardite, and chlorite. The industrial mining, milling, and processing of talc ore is associated with elevated incidences of fibrotic and neoplastic diseases, which are also seen among workers exposed to RTP in secondary industries and individuals using processed cosmetic talc for personal use. There is controversial evidence of a link between the talc-induced lung diseases and a potential contamination with asbestos fibers. This controversy is fueled by inadequate exposure data and the complex mineralogy and terminology of the accessory minerals. Talc aerosols exhibit a wide range of mineral habits, including particulates and fibrous structures that have dimensional and compositional characteristics related to the development of asbestos-related lung disease. The inhalation toxicology of RTP is based on the analysis of occupational hygiene and animal inhalation studies conducted between the 1940s and the 1990s and more recent mechanistic studies conducted both in vivo and in vitro. The review of talc toxicity studies reveals that the occupational studies provide only equivocal links between any of the components of the aerosols and the development of pulmonary cancer; however, there is substantial evidence of an association between the aerosols and pleural and pulmonary fibrosis and the development of nonmalignant respiratory disease. The animal inhalation and implantation studies appear to be less than optimal, which also appears to be true for the in vivo and in vitro studies. The mechanistic studies have identified the key pathogenic characteristics of asbestos to be long and thin fibers that are durable in lung tissues and fluids. Talc toxicity studies show that talc particles and fibers are durable and can remain in the lung for up to 40 years after the end of exposure. This extended tissue residence is considered to constitute a continuing tissue exposure that is capable of inducing the documented inflammatory and proliferative response. There is less consensus as to whether there is a threshold fiber length effect, as long, thin fibers (>5 µm) form only a small fraction of talc aerosols and the possible role of fibers >5 µm in the translocation from the lung to the pleura and their association with pleural fibrotic and carcinogenic lesions. Long, thin fibers are preferentially deposited in hot spots in the lung, such as airway bifurcations, areas typically associated with the development of lung cancer. The platy structures typical of talc can form oblate structures behaving more as fibers in the air stream, and these have also been shown to deposit preferentially in such locations. The review of the inhalation toxicity of talc provides a plausible explanation for the carcinogenic potential of RTP.

Light-harvesting mechanism of bacteria exploits a critical interplay between the dynamics of transport and trapping.

Light-harvesting bacteria Rhodospirillum photometricum were recently found to adopt strikingly different architectures depending on illumination conditions. We present analytic and numerical calculations which explain this observation by quantifying a dynamical interplay between excitation transfer kinetics and reaction center cycling. High light-intensity membranes exploit dissipation as a photoprotective mechanism, thereby safeguarding a steady supply of chemical energy, while low light-intensity membranes efficiently process unused illumination intensity by channeling it to open reaction centers. More generally, our analysis elucidates and quantifies the trade-offs in natural network design for solar energy conversion.

Unifying casualty distributions within and across conflicts.

The distribution of whole war sizes and the distribution of event sizes within individual wars, can both be well approximated by power laws where size is measured by the number of fatalities. However the power-law exponent value for whole wars has a substantially smaller magnitude - and hence a flatter distribution - than for individual wars. We provide detailed numerical evidence that confirms that these numerically different power-law exponent values are interrelated in a simple way by the effect of aggregating fatalities from individual events within wars to whole wars. We offer intuition for this finding and hence strengthen the case for a unified description and understanding of human conflict across scales.

Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning.

A huge amount of potentially dangerous COVID-19 misinformation is appearing online. Here we use machine learning to quantify COVID-19 content among online opponents of establishment health guidance, in particular vaccinations ("anti-vax"). We find that the anti-vax community is developing a less focused debate around COVID-19 than its counterpart, the pro-vaccination ("pro-vax") community. However, the anti-vax community exhibits a broader range of "flavors" of COVID-19 topics, and hence can appeal to a broader cross-section of individuals seeking COVID-19 guidance online, e.g. individuals wary of a mandatory fast-tracked COVID-19 vaccine or those seeking alternative remedies. Hence the anti-vax community looks better positioned to attract fresh support going forward than the pro-vax community. This is concerning since a widespread lack of adoption of a COVID-19 vaccine will mean the world falls short of providing herd immunity, leaving countries open to future COVID-19 resurgences. We provide a mechanistic model that interprets these results and could help in assessing the likely efficacy of intervention strategies. Our approach is scalable and hence tackles the urgent problem facing social media platforms of having to analyze huge volumes of online health misinformation and disinformation.

Energy transfer in light-adapted photosynthetic membranes: from active to saturated photosynthesis.

In bacterial photosynthesis light-harvesting complexes, LH2 and LH1 absorb sunlight energy and deliver it to reaction centers (RCs) with extraordinarily high efficiency. Submolecular resolution images have revealed that both the LH2:LH1 ratio, and the architecture of the photosynthetic membrane itself, adapt to light intensity. We investigate the functional implications of structural adaptations in the energy transfer performance in natural in vivo low- and high-light-adapted membrane architectures of Rhodospirillum photometricum. A model is presented to describe excitation migration across the full range of light intensities that cover states from active photosynthesis, where all RCs are available for charge separation, to saturated photosynthesis where all RCs are unavailable. Our study outlines three key findings. First, there is a critical light-energy density, below which the low-light adapted membrane is more efficient at absorbing photons and generating a charge separation at RCs, than the high-light-adapted membrane. Second, connectivity of core complexes is similar in both membranes, suggesting that, despite different growth conditions, a preferred transfer pathway is through core-core contacts. Third, there may be minimal subareas on the membrane which, containing the same LH2:LH1 ratio, behave as minimal functional units as far as excitation transfer efficiency is concerned.

Anomalously slow attrition times for asymmetric populations with internal group dynamics.

The many-body dynamics exhibited by living objects include group formation within a population and the nonequilibrium process of attrition between two opposing populations due to competition or conflict. We show analytically and numerically that the combination of these two dynamical processes generates an attrition duration T whose nonlinear dependence on population asymmetry x is in stark contrast to standard mass-action theories. A minority population experiences a longer survival time than two equally balanced populations, irrespective of whether or not the majority population adopts such an internal grouping. Adding a third population with predefined group sizes allows T(x) to be tailored. Our findings compare favorably to real-world observations.