Identifying and characterizing superspreaders of low-credibility content on Twitter.

The world's digital information ecosystem continues to struggle with the spread of misinformation. Prior work has suggested that users who consistently disseminate a disproportionate amount of low-credibility content-so-called superspreaders-are at the center of this problem. We quantitatively confirm this hypothesis and introduce simple metrics to predict the top superspreaders several months into the future. We then conduct a qualitative review to characterize the most prolific superspreaders and analyze their sharing behaviors. Superspreaders include pundits with large followings, low-credibility media outlets, personal accounts affiliated with those media outlets, and a range of influencers. They are primarily political in nature and use more toxic language than the typical user sharing misinformation. We also find concerning evidence that suggests Twitter may be overlooking prominent superspreaders. We hope this work will further public understanding of bad actors and promote steps to mitigate their negative impacts on healthy digital discourse.

The Social Contagion Potential of Pro-Vaccine Messages on Black Twitter.

Black Americans in the US not only suffered from disproportionately high hospitalization and death rates throughout the pandemic but also from the consequences of low COVID-19 vaccination rates. This pattern of disparity is linked to distrust of public health systems that originates from a history of medical atrocities committed against Black people. For that reason, mitigation of race-based inequity in COVID-19 impacts might find more success in grassroots information contagion than official public health campaigns. While Black Twitter is well-positioned as a conduit for such information contagion, little is known about message characteristics that would afford it. Here, we tested the impact of four different message frames (personalization, interactive, fear appeal, neutral) on the social contagion potential of bi-modal social media messages promoting COVID-19 vaccinations and finding personalized messages to be the most shareable. Wary of recommending personalization as the blueprint for setting a social contagion health campaign in motion, we probed further to understand the influence of individual-level variables on the communicability of personalized messages. Subsequently, regression models and focus group data were consulted, revealing that thinking styles, vaccine confidence levels, and attitudes toward social media were significant factors of influence on the contagion potential of personalized messages. We discussed the implications of these results for health campaigns.

One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study.

BACKGROUND: Vaccinations play a critical role in mitigating the impact of COVID-19 and other diseases. Past research has linked misinformation to increased hesitancy and lower vaccination rates. Gaps remain in our knowledge about the main drivers of vaccine misinformation on social media and effective ways to intervene. OBJECTIVE: Our longitudinal study had two primary objectives: (1) to investigate the patterns of prevalence and contagion of COVID-19 vaccine misinformation on Twitter in 2021, and (2) to identify the main spreaders of vaccine misinformation. Given our initial results, we further considered the likely drivers of misinformation and its spread, providing insights for potential interventions. METHODS: We collected almost 300 million English-language tweets related to COVID-19 vaccines using a list of over 80 relevant keywords over a period of 12 months. We then extracted and labeled news articles at the source level based on third-party lists of low-credibility and mainstream news sources, and measured the prevalence of different kinds of information. We also considered suspicious YouTube videos shared on Twitter. We focused our analysis of vaccine misinformation spreaders on verified and automated Twitter accounts. RESULTS: Our findings showed a relatively low prevalence of low-credibility information compared to the entirety of mainstream news. However, the most popular low-credibility sources had reshare volumes comparable to those of many mainstream sources, and had larger volumes than those of authoritative sources such as the US Centers for Disease Control and Prevention and the World Health Organization. Throughout the year, we observed an increasing trend in the prevalence of low-credibility news about vaccines. We also observed a considerable amount of suspicious YouTube videos shared on Twitter. Tweets by a small group of approximately 800 "superspreaders" verified by Twitter accounted for approximately 35% of all reshares of misinformation on an average day, with the top superspreader (@RobertKennedyJr) responsible for over 13% of retweets. Finally, low-credibility news and suspicious YouTube videos were more likely to be shared by automated accounts. CONCLUSIONS: The wide spread of misinformation around COVID-19 vaccines on Twitter during 2021 shows that there was an audience for this type of content. Our findings are also consistent with the hypothesis that superspreaders are driven by financial incentives that allow them to profit from health misinformation. Despite high-profile cases of deplatformed misinformation superspreaders, our results show that in 2021, a few individuals still played an outsized role in the spread of low-credibility vaccine content. As a result, social media moderation efforts would be better served by focusing on reducing the online visibility of repeat spreaders of harmful content, especially during public health crises.

Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal.

Widespread uptake of vaccines is necessary to achieve herd immunity. However, uptake rates have varied across U.S. states during the first six months of the COVID-19 vaccination program. Misbeliefs may play an important role in vaccine hesitancy, and there is a need to understand relationships between misinformation, beliefs, behaviors, and health outcomes. Here we investigate the extent to which COVID-19 vaccination rates and vaccine hesitancy are associated with levels of online misinformation about vaccines. We also look for evidence of directionality from online misinformation to vaccine hesitancy. We find a negative relationship between misinformation and vaccination uptake rates. Online misinformation is also correlated with vaccine hesitancy rates taken from survey data. Associations between vaccine outcomes and misinformation remain significant when accounting for political as well as demographic and socioeconomic factors. While vaccine hesitancy is strongly associated with Republican vote share, we observe that the effect of online misinformation on hesitancy is strongest across Democratic rather than Republican counties. Granger causality analysis shows evidence for a directional relationship from online misinformation to vaccine hesitancy. Our results support a need for interventions that address misbeliefs, allowing individuals to make better-informed health decisions.

Development and application of an innovative approach to predicting pollutant concentrations in highway runoff.

Recognising the challenges and limitations of current methodologies to predict highway runoff concentrations, this paper presents a novel approach based on the derivation of pollutant emission factors for twelve different types of vehicle. Published emission factor data and properties of differing vehicles types are combined with annual average daily traffic volume (AADT), highway characteristics and rainfall data to determine the pollutant distributions associated with differing highway and traffic types. In this paper, the method is applied to 126 sections of highway in the Greater London Borough of Enfield (United Kingdom; UK) and results are comparable with values reported in the literature. The approach is used to identify the level of AADT predicted to result in an exceedance of environmental quality standards (EQS), with results suggesting that runoff from highways experiencing AADT values as low as 5000 may require treatment prior to discharge to receiving waters. Future scenario analyses indicate that the impact of progressively replacing petrol and diesel vehicles with electric vehicles will have negligible impact on concentrations of zinc (Zn), copper (Cu), cadmium (Cd) and total suspended solids discharging from highway environments. The approach enables identification and ranking of urban highways in terms of their pollution runoff potential and provides an important support to users in prioritising locations for the installation of sustainable drainage options in order to protect receiving water environments.

Modelling transitions between egalitarian, dynamic leader and absolutist power structures.

Human groups show a variety of leadership dynamics ranging from egalitarian groups with no leader, to groups with changing leaders, to absolutist groups with a single long-term leader. Here, we model transitions between these different phases of leadership dynamics, investigating the role of inequalities in relationships between individuals. Our results demonstrate a novel riches-to-rags class of leadership dynamics where a leader can be replaced by a new individual. We note that the transition between the three different phases of leadership dynamics resembles transitions in leadership dynamics during the Neolithic period of human history. We argue how technological developments, such as food storage and/or weapons which allow one individual to control large quantities of resources, would mean that relationships became more unequal. In general terms, we provide a model of how individual relationships can affect leadership dynamics and structures.

Underlying socio-political processes behind the 2016 US election.

Recently we have witnessed a number of rapid shifts toward populism in the rhetoric and policies of major political parties, as exemplified in the 2016 Brexit Referendum, 2016 US Election, and 2017 UK General Election. Our perspective here is to focus on understanding the underlying societal processes behind these recent political shifts. We use novel methods to study social dynamics behind the 2016 Presidential election. This is done by using network science methods to identify key groups associated with the US right-wing during the election. We investigate how the groups grew on Twitter, and how their associated accounts changed their following behaviour over time. We find a new external faction of Trump supporters took a strong influence over the traditional Republican Party (GOP) base during the election campaign. The new group dominated the GOP group in terms of new members and endorsement via Twitter follows. Growth of new accounts for the GOP party all but collapsed during the campaign. While the Alt-right group was growing exponentially, it has remained relatively isolated. Counter to the mainstream view, we detected an unexpectedly low number of automated 'bot' accounts and accounts associated with foreign intervention in the Trump-supporting group. Our work demonstrates a powerful method for tracking the evolution of societal groups and reveals complex social processes behind political changes.

How humans transmit language: horizontal transmission matches word frequencies among peers on Twitter.

Language transmission, the passing on of language features such as words between people, is the process of inheritance that underlies linguistic evolution. To understand how language transmission works, we need a mechanistic understanding based on empirical evidence of lasting change of language usage. Here, we analysed 200 million online conversations to investigate transmission between individuals. We find that the frequency of word usage is inherited over conversations, rather than only the binary presence or absence of a word in a person's lexicon. We propose a mechanism for transmission whereby for each word someone encounters there is a chance they will use it more often. Using this mechanism, we measure that, for one word in around every hundred a person encounters, they will use that word more frequently. As more commonly used words are encountered more often, this means that it is the frequencies of words which are copied. Beyond this, our measurements indicate that this per-encounter mechanism is neutral and applies without any further distinction as to whether a word encountered in a conversation is commonly used or not. An important consequence of this is that frequencies of many words can be used in concert to observe and measure language transmission, and our results confirm this. These results indicate that our mechanism for transmission can be used to study language patterns and evolution within populations.

Interspecific competition in honeybee intracellular gut parasites is asymmetric and favours the spread of an emerging infectious disease.

There is increasing appreciation that hosts in natural populations are subject to infection by multiple parasite species. Yet the epidemiological and ecological processes determining the outcome of mixed infections are poorly understood. Here, we use two intracellular gut parasites (Microsporidia), one exotic and one co-evolved in the western honeybee (Apis mellifera), in an experiment in which either one or both parasites were administered either simultaneously or sequentially. We provide clear evidence of within-host competition; order of infection was an important determinant of the competitive outcome between parasites, with the first parasite significantly inhibiting the growth of the second, regardless of species. However, the strength of this 'priority effect' was highly asymmetric, with the exotic Nosema ceranae exhibiting stronger inhibition of Nosema apis than vice versa. Our results reveal an unusual asymmetry in parasite competition that is dependent on order of infection. When incorporated into a mathematical model of disease prevalence, we find asymmetric competition to be an important predictor of the patterns of parasite prevalence found in nature. Our findings demonstrate the wider significance of complex multi-host-multi-parasite interactions as drivers of host-pathogen community structure.

Chronic sublethal stress causes bee colony failure.

Current bee population declines and colony failures are well documented yet poorly understood and no single factor has been identified as a leading cause. The evidence is equivocal and puzzling: for instance, many pathogens and parasites can be found in both failing and surviving colonies and field pesticide exposure is typically sublethal. Here, we investigate how these results can be due to sublethal stress impairing colony function. We mathematically modelled stress on individual bees which impairs colony function and found how positive density dependence can cause multiple dynamic outcomes: some colonies fail while others thrive. We then exposed bumblebee colonies to sublethal levels of a neonicotinoid pesticide. The dynamics of colony failure, which we observed, were most accurately described by our model. We argue that our model can explain the enigmatic aspects of bee colony failures, highlighting an important role for sublethal stress in colony declines.

Dynamic transmission, host quality, and population structure in a multihost parasite of bumblebees.

The evolutionary ecology of multihost parasites is predicted to depend upon patterns of host quality and the dynamics of transmission networks. Depending upon the differences in host quality and transmission asymmetries, as well as the balance between intra- and interspecific transmission, the evolution of specialist or generalist strategies is predicted. Using a trypanosome parasite of bumblebees, we ask how host quality and transmission networks relate to parasite population structure across host species, and thus the potential for the evolution of specialist strains adapted to different host species. Host species differed in quality, with parasite growth varying across host species. Highly asymmetric transmission networks, together with differences in host quality, likely explain local population structure of the parasite across host species. However, parasite population structure across years was highly dynamic, with parasite populations varying significantly from one year to the next within individual species at a given site. This suggests that, while host quality and transmission may provide the opportunity for short-term host specialization by the parasite, repeated bottlenecking of the parasite, in combination with its own reproductive biology, overrides these smaller scale effects, resulting in the evolution of a generalist parasite.

Stability in flux: community structure in dynamic networks.

The structure of many biological, social and technological systems can usefully be described in terms of complex networks. Although often portrayed as fixed in time, such networks are inherently dynamic, as the edges that join nodes are cut and rewired, and nodes themselves update their states. Understanding the structure of these networks requires us to understand the dynamic processes that create, maintain and modify them. Here, we build upon existing models of coevolving networks to characterize how dynamic behaviour at the level of individual nodes generates stable aggregate behaviours. We focus particularly on the dynamics of groups of nodes formed endogenously by nodes that share similar properties (represented as node state) and demonstrate that, under certain conditions, network modularity based on state compares well with network modularity based on topology. We show that if nodes rewire their edges based on fixed node states, the network modularity reaches a stable equilibrium which we quantify analytically. Furthermore, if node state is not fixed, but can be adopted from neighbouring nodes, the distribution of group sizes reaches a dynamic equilibrium, which remains stable even as the composition and identity of the groups change. These results show that dynamic networks can maintain the stable community structure that has been observed in many social and biological systems.

The impact of clonal mixing on the evolution of social behaviour in aphids.

Reports of substantial clonal mixing measured in social aphid colonies seem, on the face of it, to rule out population structure as an explanation of this enigmatic insect's social behaviour. To clarify how selection operates in aphids, and to disentangle direct and indirect fitness components, we present a model of the life cycle of a typical colony-dwelling aphid. The model incorporates ecological factors and includes a trade-off between investing in social behaviour and investing in reproduction. Our focus on inclusive fitness contrasts with previous approaches that optimize colony output. Through deriving a variant of Hamilton's rule, we show that a simple relationship can be established between the patch-carrying capacity and immigration rates into patches. Our results indicate that the levels of clonal mixing reported are not inconsistent with social behaviour. We discuss our model in terms of the evolutionary origins of social behaviour in aphids.

Neural control of Caenorhabditis elegans forward locomotion: the role of sensory feedback.

This paper presents a simple yet biologically-grounded model for the neural control of Caenorhabditis elegans forward locomotion. We identify a minimal circuit within the C. elegans ventral cord that is likely to be sufficient to generate and sustain forward locomotion in vivo. This limited subcircuit appears to contain no obvious central pattern generated control. For that subcircuit, we present a model that relies on a chain of oscillators along the body which are driven by local and proximate mechano-sensory input. Computer simulations were used to study the model under a variety of conditions and to test whether it is behaviourally plausible. Within our model, we find that a minimal circuit of AVB interneurons and B-class motoneurons is sufficient to generate and sustain fictive forward locomotion patterns that are robust to significant environmental perturbations. The model predicts speed and amplitude modulation by the AVB command interneurons. An extended model including D-class motoneurons is included for comparison.

Managing change in health informatics--lessons learned from a third of a century of change. Introduction.

This short paper describes strategies and tactics that have been used for introducing Health Informatics projects in Scotland, UK during the last almost 40 years. This is a country of only 5 million population. It has a scattered geography with isolated islands and moorland and hilly areas along with a populous former industrial heartland. It has a unified National Health Service, free at each point of contact. By many measurements it is not a healthy population with traditions of unhealthy life-styles.

Health information change management lessons--learned from a third of a century of change introduction.

This paper concentrates on the disincentives that discourage particularly doctors from readily adopting new health informatics techniques and suggests that health project management is a different model of management from that used in other industries.