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The theory behind network science predated the internet, but the rise of social media was a major cultural innovation that begged for a science of how people are connected. And while there are a plethora of fun and interesting questions about how people interact, few have been more relevant than how social movements came about.
Take this year’s #Hashtag Activism, for example, in which Brooke Foucault Welles, Sarah Jackson, and Moya Bailey use the science of networks to uncover the growth of social media activism.
Foucault Welles, associate professor at Northeastern, says network science “allows us to distill vast and chaotic online communication data to its essence” and “extract important themes, people and events for careful reading.” This intersection with big data is fundamental: the fact of being able to extract models from terabytes of interactions on social media reinforces the scope of his conclusions: the results do not concern the behavior of a small group of users, but the aggregate behavior.
The approaches highlighted in #Hashtag Activism it can reveal the fundamental principles of social movements that apply to the digital activism movements of recent times. From a network of activist narratives built from quantitative and qualitative data, Foucault Welles describes how, “In #MeToo, we found that talking about online sexual violence is really powerful because it reduces stigma and encourages other people to reveal. people to come forward have to be really brave and talk about what happened to them, even though they may not be believed, they may not be supported and they may be blamed. But whenever someone is brave and comes forward, it reduces the risk of others people come forward. ” The work of Foucault Welles and colleagues provides part of a blueprint for how to build hashtag movements moving forward. “In every social justice movement,” he says, “there is a committed core of activists who work hard to create and spread a message. Then there’s a huge periphery of allies and supporters amplifying that message. I love this discovery because it shows how activists and ordinary people can work hand in hand, like us to have working hand in hand to keep things going “.
While social movements have recently come into the crosshairs of network science, the field has long focused on epidemiology. It takes little imagination to consider how a science dedicated to understanding how connections between people matter in infectious diseases. Network science has led to a slew of breakthroughs in epidemiology, from identifying the role of aviation in the global spread of epidemics, to revealing how replacing sick workers with healthy workers can drive the dynamics of influenza.
The dynamics of Covid-19 proved particularly difficult to understand, as questions persisted about the importance of asymptomatic transmission and super-diffusion events. The network perspective has added layers to how we look at the basic aspects of an epidemic, such as the basic reproduction number (R0), a signature of contagiousness. The study of networks shows that this number is truly an average and does not consider how selected individuals embedded in a network can infect others in much larger numbers than predicted by R0.
Dina Mistry, a postdoctoral fellow at the Institute for Disease Modeling, has conducted pioneering work on human interaction networks and social mixing models. That is, it builds careful and detailed simulations of exactly how people interact, to inform public health intervention models, all of which are highly relevant to the Covid-19 pandemic.
“We don’t know how to model contact patterns, especially in metropolitan areas and in families,” says Mistry. A job like this is key to conversations about contact tracing, the safe reopening of schools, and other central conversations that arose during the Covid-19 pandemic. Mistry further suggests that it is important to “collect and report on data distribution, rather than point estimates. For example, if we think this way, maybe we can explore heterogeneity in things like behavior adoption: I want to know more. of the simple percentage of people who adopt a behavior, rather what is the distribution of the willingness to adopt behaviors, for example, wearing the mask and the covariates that accompany it “.
Network science and our dangerous future
The cases of Foucault Welles and Mistry demonstrate the fungibility of network science and the importance of integrating theory with data science, which help in their ability to describe large and complicated schemes. But the real measure of a field lies in what it offers for the future.
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