Right and left, partisanship predicts (asymmetric) vulnerability to misinformation

We analyze the relationship between partisanship, echo chambers, and vulnerability to online misinformation by studying news sharing behavior on Twitter. While our results confirm prior findings that online misinformation sharing is strongly correlated with right-leaning partisanship, we also uncover a similar, though weaker, trend among left-leaning users. Because of the correlation between a user’s partisanship and their position within a partisan echo chamber, these types of influence are confounded. To disentangle their effects, we performed a regression analysis and found that vulnerability to misinformation is most strongly influenced by partisanship for both left- and right-leaning users.

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Mutual anticipation can contribute to self-organization in human crowds

Human crowds provide paradigmatic examples of collective behavior emerging through self-organization. Understanding their dynamics is crucial to help manage mass events and daily pedestrian transportation. Although recent findings emphasized that pedestrians’ interactions are fundamentally anticipatory in nature, whether and how individual anticipation functionally benefits the group is not well understood. Here, we show the link between individual anticipation and emergent pattern formation through our experiments of lane formation, where unidirectional lanes are spontaneously formed in bidirectional pedestrian flows. Manipulating the anticipatory abilities of some of the pedestrians by distracting them visually delayed the collective pattern formation. Moreover, both the distracted pedestrians and the nondistracted ones had difficulties avoiding collisions while navigating. These results imply that avoidance maneuvers are normally a cooperative process and that mutual anticipation between pedestrians facilitates efficient pattern formation. Our findings may influence various fields, including traffic management, decision-making research, and swarm dynamics.

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Complexity and Self-Organization

Complexity occurs when relevant interactions prevent the study of elements of a system in isolation. These interactions between elements may lead to the self-organization of the system. A system can be described as self-organizing when its global properties are a product of the interactions of its components. Complexity and self-organization are prevalent in a broad variety of systems. Because of this, they have been studied from multiple perspectives and disciplines, leading naturally to transdisciplinary studies.

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Complexity and Self-organization

Exacerbating Inequalities: Social Networks, Racial/Ethnic Disparities, and the COVID-19 Pandemic

The disruption and contraction of older adults’ social networks are among the less discussed consequences of the COVID-19 pandemic. Our objective was to provide an evidence-based commentary on racial/ethnic disparities in social network resources and draw attention to the ways in which disasters differentially affect social networks, with meaningful insight for the ongoing pandemic.

We draw upon prior research on social networks and past natural disasters to identify major areas of network inequality. Attention is given to how pre-pandemic racial/ethnic network disparities are exacerbated during the current crisis, with implications for physical and mental health outcomes.

Evidence from the literature shows a robust association between strong social networks and physical and mental health outcomes. During times of crisis, access to social networks for older adults is disrupted, particularly for marginalized groups. We document pre-pandemic disparities in social networks resources and offer insight for examining the impact of COVID-19 on disrupting social networks among older adults.

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Social Networks’ Engagement During the COVID-19 Pandemic

An increased use of social networks is one of the most far-reaching consequences of the COVID-19 pandemic. Aside from the traditional media, as the main drivers of social communication in crisis situations, individual profiles have emerged supported by social networks, which have had a similar impact to the more specialized communication media. This is the hypothesis of the research presented, which is focused on health communication and based on a virtual ethnography methodology with the use of social metrics. The aim is to understand the relationship established between the population in general and digital media in particular through the measurement of engagement. In this regard, a comparative study was carried out that describes this phenomenon over a period of six months on three social networks: YouTube, Twitter and Instagram, with a sample composed of specialized health media versus healthcare professionals. The results point to a new communications model that opens up a new space for agents whose content has a degree of engagement comparable to and even exceeding that of digital media specialized in health communication. The conclusions show that the crisis of the pandemic has accelerated the transformation of the communication sector, creating new challenges for the communication industry, media professionals, and higher education institutions related to market demands.

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Social Networks and Loneliness During the COVID-19 Pandemic

We utilize longitudinal social network data collected pre–COVID-19 in June 2019 and compare them with data collected in the midst of COVID in June 2020. We find significant decreases in network density and global network size following a period of profound social isolation. While there is an overall increase in loneliness during this era, certain social network characteristics of individuals are associated with smaller increases in loneliness. Specifically, we find that people with fewer than five “very close” relationships report increases in loneliness. We further find that face-to-face interactions, as well as the duration and frequency of interactions with very close ties, are associated with smaller increases in loneliness during the pandemic. We also report on factors that do not moderate the effect of social isolation on perceived loneliness, such as gender, age, or overall social network size.

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A social network model of COVID-19

I construct a dynamic social-network model of the COVID-19 epidemic which embeds the SIR epidemiological model onto a graph of person-to-person interactions. The standard SIR framework assumes uniform mixing of infectious persons in the population. This abstracts from important elements of realism and locality: (i) people are more likely to interact with members of their social networks and (ii) health and economic policies can affect differentially the rate of viral transmission via a person’s social network vs. the population as a whole. The proposed network-augmented (NSIR) model allows the evaluation, via simulations, of (i) health and economic policies and outcomes for all or subset of the population: lockdown/distancing, herd immunity, testing, contact tracing; (ii) behavioral responses and/or imposing or lifting policies at specific times or conditional on observed states. I find that viral transmission over a network-connected population can proceed slower and reach lower peak than transmission via uniform mixing. Network connections introduce uncertainty and path dependence in the epidemic dynamics, with a significant role for bridge links and superspreaders. Testing and contact tracing are more effective in the network model. If lifted early, distancing policies mostly shift the infection peak into the future, with associated economic costs. Delayed or intermittent interventions or endogenous behavioral responses generate a multi-peaked infection curve, a form of ‘curve flattening’, but may have costlier economic consequences by prolonging the epidemic duration.

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Covid time – Young People’s Mental and Emotional Health

Given the known rise in prevalence of mental illness from childhood to adolescence, particularly in girls, this analysis aims to dig more deeply into young people’s mental health and wellbeing in this period of life. Poor mental health in adolescence is strongly associated with poor mental health in adulthood, which, in turn, can affect relationships, societal engagement and productivity. Since the arrival of Covid-19, the prevalence of probable mental disorders has risen substantially to one in six young people, from one in nine in 2017.

We find that as children move into adolescence, self-esteem is more strongly correlated with both wellbeing and levels of psychological distress, suggesting that as young people get older, how they see and value themselves becomes more closely tied to how they feel about their lives generally. This is of particular concern for girls, a significant proportion of whom struggle with body image issues and lower self-esteem.

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Covid-19 recovery and resilience: what can health and care learn from other disasters?

As Covid-19 swept the globe, countries scrambled to tackle the immediate threat of the virus. Entire new hospitals were built in just days, people have been required to restrict their activities on a scale previously inconceivable during peacetime and a new class of vaccine was developed, trialled and approved within a matter of months. The scale of the emergency response has been extraordinary. But what comes next?

When a disaster, such as an earthquake, a flood or a pandemic, hits emergency plans are quickly enacted and command-and-control structures mobilised. But how to manage recovery isn’t always so clear. How do individuals, communities and countries recover from catastrophic events? How do we know what support is needed, which groups should be prioritised and how should efforts be co-ordinated and managed? And what role should the health and care system play in recovery?

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In the wake of the pandemic Preparing for Long COVID

Over the past year the world has tracked the progress of the COVID-19 pandemic using data on cases and deaths. Yet we now know that these provide only a partial picture. Many people struggle to recover from the acute infection, suffering often disabling symptoms that last weeks or months and, in some cases, with disabilities that are likely to be very long lasting. Our understanding of this new condition, now termed Long COVID, is growing rapidly. For this, we owe a great deal to many people, but especially to those affected who have come together to document, analyse and report on the complex nature of this condition and its impact on their lives, as well as the health professionals, in some cases themselves suffering from Long COVID, who have initiated important research projects.

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