Damon Centola is a Professor of Communication, Sociology and Engineering at the University of Pennsylvania, where he is Director of the Network Dynamics Group. He is a leading world expert on social networks and behavior change. His work has been published in Science, Nature Human Behavior, Proceedings of the National Academy of Sciences, American Journal of Sociology, Circulation and Journal of Statistical Physics. Damon received the American Sociological Association’s Award for Outstanding Research in Mathematical Sociology in 2006, 2009, and 2011; the Goodman Prize for Outstanding Contribution to Sociological Methodology in 2011; the James Coleman Award for Outstanding Research in Rationality and Society in 2017; and the Harrison White Award for Outstanding Scholarly Book in 2019. He was a developer of the NetLogo agent based modeling environment, and was awarded a U.S. Patent for inventing a method to promote diffusion in online networks. He is a member of the Sci Foo community and Fellow of the Center for Advanced Study in the Behavioral Sciences at Stanford University. Popular accounts of Damon’s work have appeared in the New York Times, the Washington Post, The Wall Street Journal, Wired, TIME, The Atlantic, Scientific American and CNN. His speaking and consulting clients include Amazon, Microsoft, Apple, Cigna, the Smithsonian Institution, the American Heart Association, the National Academies, the U.S. Army and the NBA. Before coming to Penn, Damon was an Assistant Professor at M.I.T. and a Robert Wood Johnson Fellow at Harvard University. His research is funded by the National Science Foundation, the Robert Wood Johnson Foundation, the National Institutes of Health, the James S. McDonnell Foundation, and the Hewlett Foundation. He is a series editor for Princeton University Press, and the author of How Behavior Spreads, and Change: How to Make Big Things Happen.
Danny Valdez, Marijn ten Thij, Krishna Bathina, Lauren A Rutter, Johan Bollen
J Med Internet Res 2020;22(12):e21418
Background: The COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a challenge to the world’s mental health and health care systems. Considering that traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population’s mental health. Many people in the United States now use social media platforms such as Twitter to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field to understand personal well-being.
Objective: This study aims to answer three research questions: (1) What themes emerge from a corpus of US tweets about COVID-19? (2) To what extent did social media use increase during the onset of the COVID-19 pandemic? and (3) Does sentiment change in response to the COVID-19 pandemic?
Methods: We analyzed 86,581,237 public domain English language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using latent Dirichlet allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n=354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool.
Results: LDA topics generated in the early months of the data set corresponded to major COVID-19–specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted toward US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis scores of user timelines were initially high and stable but decreased significantly, and continuously, by late March.
Conclusions: Our findings underscore the negative effects of the pandemic on overall population sentiment. Increased use rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media use on mental health, social media may further exacerbate negative feelings in the long-term for many individuals. Thus, considering the overburdened US mental health care structure, these findings have important implications for ongoing mitigation efforts.
Deborah Frieze speaks at the Networking for Community Resilience event on Tuesday 22nd November 2011 at the Greenhouse Dublin Ireland. http://www.cultivate.ie
Recently a long-time member of the Food Solutions New England (FNSE) Network Team let us know that they would be transitioning out of their current job and needing to leave the network, at least the core role they have played. FSNE is entering its second, and critical, decade of work, and going through a transition... Read More
Mohsen Mosleh, Cameron Martel, Dean Eckles, David G. Rand
Americans are much more likely to be socially connected to co-partisans, both in daily life and on social media. But this observation does not necessarily mean that shared partisanship per se drives social tie formation, because partisanship is confounded with many other factors. Here, we test the causal effect of shared partisanship on the formation of social ties in a field experiment on Twitter. We created bot accounts that self-identified as people who favored the Democratic or Republican party, and that varied in the strength of that identification. We then randomly assigned 842 Twitter users to be followed by one of our accounts. Users were roughly three times more likely to reciprocally follow-back bots whose partisanship matched their own, and this was true regardless of the bot’s strength of identification. Interestingly, there was no partisan asymmetry in this preferential follow-back behavior: Democrats and Republicans alike were much more likely to reciprocate follows from co-partisans. These results demonstrate a strong causal effect of shared partisanship on the formation of social ties in an ecologically valid field setting, and have important implications for political psychology, social media, and the politically polarized state of the American public.
Dan Braha Scientific Reports volume 10, Article number: 18137 (2020)
Understanding the functions carried out by network subgraphs is important to revealing the organizing principles of diverse complex networks. Here, we study this question in the context of collaborative problem-solving, which is central to a variety of domains from engineering and medicine to economics and social planning. We analyze the frequency of all three- and four-node subgraphs in diverse real problem-solving networks. The results reveal a strong association between a dynamic property of network subgraphs—synchronizability—and the frequency and significance of these subgraphs in problem-solving networks. In particular, we show that highly-synchronizable subgraphs are overrepresented in the networks, while poorly-synchronizable subgraphs are underrepresented, suggesting that dynamical properties affect their prevalence, and thus the global structure of networks. We propose the possibility that selective pressures that favor more synchronizable subgraphs could account for their abundance in problem-solving networks. The empirical results also show that unrelated problem-solving networks display very similar local network structure, implying that network subgraphs could represent organizational routines that enable better coordination and control of problem-solving activities. The findings could also have potential implications in understanding the functionality of network subgraphs in other information-processing networks, including biological and social networks.
Serina Chang, Emma Pierson, Pang Wei Koh, Jaline Gerardin, Beth Redbird, David Grusky & Jure Leskovec Nature (2020)
The COVID-19 pandemic dramatically changed human mobility patterns, necessitating epidemiological models which capture the effects of changes in mobility on virus spread1. We introduce a metapopulation SEIR model that integrates fine-grained, dynamic mobility networks to simulate the spread of SARS-CoV-2 in 10 of the largest US metropolitan statistical areas. Derived from cell phone data, our mobility networks map the hourly movements of 98 million people from neighborhoods (census block groups, or CBGs) to points of interest (POIs) such as restaurants and religious establishments, connecting 57k CBGs to 553k POIs with 5.4 billion hourly edges. We show that by integrating these networks, a relatively simple SEIR model can accurately fit the real case trajectory, despite substantial changes in population behavior over time. Our model predicts that a small minority of “superspreader” POIs account for a large majority of infections and that restricting maximum occupancy at each POI is more effective than uniformly reducing mobility. Our model also correctly predicts higher infection rates among disadvantaged racial and socioeconomic groups2–8 solely from differences in mobility: we find that disadvantaged groups have not been able to reduce mobility as sharply, and that the POIs they visit are more crowded and therefore higher-risk. By capturing who is infected at which locations, our model supports detailed analyses that can inform more effective and equitable policy responses to COVID-19.
The increased complexity of infrastructure systems has resulted in critical interdependencies between multiple networks---communication systems require electricity, while the normal functioning of the power grid relies on communication systems. These interdependencies have inspired an extensive literature on coupled multilayer networks, assuming that a component failure in one network causes failures in the other network, a hard interdependence that results in a cascade of failures across multiple systems. While empirical evidence of such hard coupling is limited, the repair and recovery of a network requires resources typically supplied by other networks, resulting in well documented interdependencies induced by the recovery process. If the support networks are not functional, recovery will be slowed. Here we collected data on the recovery time of millions of power grid failures, finding evidence of universal nonlinear behavior in recovery following large perturbations. We develop a theoretical framework to address recovery coupling, predicting quantitative signatures different from the multilayer cascading failures. We then rely on controlled natural experiments to separate the role of recovery coupling from other effects like resource limitations, offering direct evidence of how recovery coupling affects a system's functionality. The resulting insights have implications beyond infrastructure systems, offering insights on the fragility and senescence of biological systems.
Our world is becoming increasingly interconnected and our relationships more complex. We have the opportunity to focus on what's close to us, what brings us together and what our common vision for the future is. At Close Knit we think, write about and create products for communities.
A virtual event last week brought together community leaders from across Bastrop County to promote "network weaving" and advance health equity among county residents.Network weaving is described as "connecting people to people and networks to other networks," officials said during the virtual event. A network weaver is "someone who senses where there is potential or opportunity waiting to be ignited or invited."The event, titled "The Power of Network Weaving: Catalyzing Community Transformation,
James Bell, Ginestra Bianconi, David Butler, Jon Crowcroft, Paul C.W Davies, Chris Hicks, Hyunju Kim, Istvan Z. Kiss, Francesco Di Lauro, Carsten Maple, Ayan Paul, Mikhail Prokopenko, Philip Tee, Sara I. Walker
On May 28th and 29th, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc. The aim was to bring together leading scientists with an interest in Network Science and Epidemiology to attempt to inform public policy in response to the COVID-19 pandemic. Epidemics are at their core a process that progresses dynamically upon a network, and are a key area of study in Network Science. In the course of the workshop a wide survey of the state of the subject was conducted. We summarize in this paper a series of perspectives of the subject, and where the authors believe fruitful areas for future research are to be found.
There’s something strange about this coronavirus pandemic. Even after months of extensive research by the global scientific community, many questions remain open.
Why, for instance, was there such an enormous death toll in northern Italy, but not the rest of the country? Just three contiguous regions in northern Italy have 25,000 of the country’s nearly 36,000 total deaths; just one region, Lombardy, has about 17,000 deaths. Almost all of these were concentrated in the first few months of the outbreak. What happened in Quito, Ecuador, in April, when so many thousands died so quickly that bodies were abandoned in the sidewalks and streets? Why, in the spring of 2020, did so few cities account for a substantial portion of global deaths, while many others with similar density, weather, age distribution, and travel patterns were spared? What can we really learn from Sweden, hailed as a great success by some because of its low case counts and deaths as the rest of Europe experiences a second wave, and as a big failure by others because it did not lock down and suffered excessive death rates earlier in the pandemic? Why did widespread predictions of catastrophe in Japan not bear out? The baffling examples go on.
Damon Centola is a Professor of Communication, Sociology and Engineering at the University of Pennsylvania, where he is Director of the Network Dynamics Group. He is a leading world expert on social networks and behavior change. His work has been published in Science, Nature Human Behavior, Proceedings of the National Academy of Sciences, American Journal of Sociology, Circulation and Journal of Statistical Physics. Damon received the American Sociological Association’s Award for Outstanding Research in Mathematical Sociology in 2006, 2009, and 2011; the Goodman Prize for Outstanding Contribution to Sociological Methodology in 2011; the James Coleman Award for Outstanding Research in Rationality and Society in 2017; and the Harrison White Award for Outstanding Scholarly Book in 2019. He was a developer of the NetLogo agent based modeling environment, and was awarded a U.S. Patent for inventing a method to promote diffusion in online networks. He is a member of the Sci Foo community and Fellow of the Center for Advanced Study in the Behavioral Sciences at Stanford University. Popular accounts of Damon’s work have appeared in the New York Times, the Washington Post, The Wall Street Journal, Wired, TIME, The Atlantic, Scientific American and CNN. His speaking and consulting clients include Amazon, Microsoft, Apple, Cigna, the Smithsonian Institution, the American Heart Association, the National Academies, the U.S. Army and the NBA. Before coming to Penn, Damon was an Assistant Professor at M.I.T. and a Robert Wood Johnson Fellow at Harvard University. His research is funded by the National Science Foundation, the Robert Wood Johnson Foundation, the National Institutes of Health, the James S. McDonnell Foundation, and the Hewlett Foundation. He is a series editor for Princeton University Press, and the author of How Behavior Spreads, and Change: How to Make Big Things Happen.
Objective We analyzed the scientific output after COVID-19 and contrasted it with studies published in the aftermath of seven epidemics/pandemics: Severe Acute Respiratory Syndrome (SARS), Influenza A virus H5N1 and Influenza A virus H1N1 human infections, Middle East Respiratory Syndrome (MERS), Ebola virus disease, Zika virus disease, and Dengue.
Design/Methodology/Approach We examined bibliometric measures for COVID-19 and the rest of studied epidemics/pandemics. Data were extracted from Web of Science, using its journal classification scheme as a proxy to quantify the multidisciplinary coverage of scientific output. We proposed a novel Thematic Dispersion Index (TDI) for the analysis of pandemic early stages.
Results/Discussion The literature on the seven epidemics/pandemics before COVID-19 has shown explosive growth of the scientific production and continuous impact during the first three years following each emergence or re-emergence of the specific infectious disease. A subsequent decline was observed with the progressive control of each health emergency. We observed an unprecedented growth in COVID-19 scientific production. TDI measured for COVID-19 (29,4) in just six months, was higher than TDI of the rest (7,5 to 21) during the first three years after epidemic initiation.
Conclusions COVID-19 literature showed the broadest subject coverage, which is clearly a consecuence of its social, economic, and political impact. The proposed indicator (TDI), allowed the study of multidisciplinarity, differentiating the thematic complexity of COVID-19 from the previous seven epidemics/pandemics.
Originality/Value The multidisciplinary nature and thematic complexity of COVID-19 research were successfully analyzed through a scientometric perspective.
Christopher Torres-Lugo, Kai-Cheng Yang, Filippo Menczer
A growing body of evidence points to critical vulnerabilities of social media, such as the emergence of partisan echo chambers and the viral spread of misinformation. We show that these vulnerabilities are amplified by abusive behaviors associated with so-called ''follow trains'' on Twitter, in which long lists of like-minded accounts are mentioned for others to follow. This leads to the formation of highly dense and hierarchical echo chambers. We present the first systematic analysis of U.S. political train networks, which involve many thousands of hyper-partisan accounts. These accounts engage in various suspicious behaviors, including some that violate platform policies: we find evidence of inauthentic automated accounts, artificial inflation of friends and followers, and abnormal content deletion. The networks are also responsible for amplifying toxic content from low-credibility and conspiratorial sources. Platforms may be reluctant to curb this kind of abuse for fear of being accused of political bias. As a result, the political echo chambers manufactured by follow trains grow denser and train accounts accumulate influence; even political leaders occasionally engage with them.
All epistemic agents physically consist of parts that must somehow comprise an integrated cognitive self. Biological individuals consist of subunits (organs, cells, and molecular networks) that are themselves complex and competent in their own native contexts. How do coherent biological Individuals result from the activity of smaller sub-agents? To understand the evolution and function of metazoan creatures’ bodies and minds, it is essential to conceptually explore the origin of multicellularity and the scaling of the basal cognition of individual cells into a coherent larger organism. In this article, I synthesize ideas in cognitive science, evolutionary biology, and developmental physiology toward a hypothesis about the origin of Individuality: “Scale-Free Cognition.” I propose a fundamental definition of an Individual based on the ability to pursue goals at an appropriate level of scale and organization and suggest a formalism for defining and comparing the cognitive capacities of highly diverse types of agents. Any Self is demarcated by a computational surface – the spatio-temporal boundary of events that it can measure, model, and try to affect. This surface sets a functional boundary - a cognitive “light cone” which defines the scale and limits of its cognition. I hypothesize that higher level goal-directed activity and agency, resulting in larger cognitive boundaries, evolve from the primal homeostatic drive of living things to reduce stress – the difference between current conditions and life-optimal conditions. The mechanisms of developmental bioelectricity - the ability of all cells to form electrical networks that process information - suggest a plausible set of gradual evolutionary steps that naturally lead from physiological homeostasis in single cells to memory, prediction, and ultimately complex cognitive agents, via scale-up of the basic drive of infotaxis. Recent data on the molecular mechanisms of pre-neural bioelectricity suggest a model of how increasingly sophisticated cognitive functions emerge smoothly from cell-cell communication used to guide embryogenesis and regeneration. This set of hypotheses provides a novel perspective on numerous phenomena, such as cancer, and makes several unique, testable predictions for interdisciplinary research that have implications not only for evolutionary developmental biology but also for biomedicine and perhaps artificial intelligence and exobiology.
I am struck by how the network building and weaving field has really mushroomed over the past several years, and with it, so much learning around approaches, structures, roles, strategy, etc. I regularly hear myself say that there is no one right way to go about “net work” for change (which is why I regularly... Read More
A conversation among advanced practitioners on the importance of having an aspirational identity to strive toward: a positive vision for who we are and who we can become, on an individual and collective level.
(...) But as the pandemic forced many conferences to adopt virtual formats, the option to attend from home—often with discounted or free registration—led to surges in participation. A survey by Science Careers of 10 U.S.-based meetings of scientific societies across a variety of disciplines showed that most saw higher—and perhaps more diverse—attendance than in previous years. The Conference on Lasers and Electro-Optics (commonly referred to as “CLEO”) and the American Association for Cancer Research Annual Meeting—both of which offered free access—showed the greatest attendance increases, growing approximately fivefold to about 20,000 and 100,000 attendees, respectively.
But virtual conferences may not serve the needs of all scientific communities. The Joint Statistical Meetings (JSM), for example, was smaller than it had been in previous years, despite offering a registration discount of about 70%. “Some might still value face-to-face interactions,” (...)
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