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Friday, April 12, 2024

The Science That Spans #MeToo, Memes, and Covid-19

The technical term is “directed onion decomposition.”

It describes how centrally embedded an individual is in a network of others. The deeper in this “onion” they are, the more connections they have. The network being studied: NHL Hockey fights.

Researchers at the University of Vermont, the University of Colorado Boulder, and Dartmouth College analyzed 10 years of hockey fight data and reconstructed these brawls into a network where lines were drawn between participants. They found that hockey enforcers who were more centrally connected to others through combat tended to be stronger fighters.

Because “enforcers”—whose primary role is to protect their teammates, intimidate opponents, and fight—are a small proportion of hockey players, they provide a model for how network structure can reveal features of how people who participate in non-normative behaviors function in a “society.”

Hockey fights are the latest phenomenon subject to the increasingly omnipotent lens of network science, a field that aims to understand how people, objects, and information are linked, and how connections create complicated phenomena, from viral videos, to pandemics, to metabolic disease. While the field is well established, 2020 might be the year where it came of age, and became essential to grasping our increasingly complicated, interconnected world.

From Tipping Points to #Hashtag Activism and Covid-19

If network science has ever been a topic of conversation in your local barber shop, we have Malcom Gladwell to thank. In 2001, The Tipping Point introduced many to the concept of social contagion, and soon became required reading in business school classes and job training. Some of network science’s earliest ideas can be traced to the 1930s, but the field as we know it can be divided into a pre- and post- “Watts and Strogatz” era, a reference to the 1998 manuscript (“Collective dynamics of small world networks”) that condensed decades of theory into the idea that the most relevant networks were neither completely structured nor totally random. It also proposed models that could be applied across many real-world systems and settings.

The “science” that followed suggests there are distinct types of networks and rules that govern how information spreads across them. Whether we’re talking about internet dating or neonatal intensive care one can examine a system with connected parts with a set of principles that provide a place to start in understanding how the many connections between “things” contribute to phenomena that we care about. Several features account for the field’s rapid efflorescence.

  1. It resembles a “physics.” To call something a “physics” is to suggest that it has fundamental principles that can be described with transparent rules, often with mathematics. For all of the perils of “mathiness” (the propensity to think that something is real simply because it is colored by math), the mathematics of network science provides a transparent language through which one can describe a part of the world in a manner that can be evaluated, refuted, or verified. There are few black boxes—if I think an idea in network science is stupid, there is a formal vocabulary to explain why.
  2. It’s fungible. Not unlike modern mathematics and physics, network science’s applications are largely limited to the imagination of the researcher. Any problem defined by connections between actors or information is potential fodder. A researcher may apply the same fundamental network principles to the spread of misinformation, a group of people who inject drugs, and … hockey fights.
  3. It’s easy to translate to non-experts. In dreaming up The Tipping Point, Malcom Gladwell may have chosen certain pop culture examples for any number of reasons. And we need not invoke anything other than his ability to write effectively for broad audiences to explain the book’s success. But unlike many scientific disciplines driven by dogma, the “networks” that network science examines are readily digested by the expert or non-expert alike: We all know what it means to be connected to something, and need little convincing that these connections are important to understanding the world. The notion that romantic partners and fan fiction and Beyonce memes are all dominated by such connections is easy to sell.


Its many past successes notwithstanding, in 2020, network science evolved from being interesting to indispensable. From Covid-19 to social justice activism, the year has been defined by a series of events that have been examined by network scientists and served as a test of both the field’s rigor and pliability.

Network science’s underlying theory predates the internet, but social media’s rise was an important cultural innovation that implored the need for a science of how people are connected. And while there are myriad fun and interesting questions about the way that people interact, few have been more pertinent than how social movements are born.

Take this year’s #Hashtag Activism, for example, in which Brooke Foucault Welles, Sarah Jackson, and Moya Bailey use network science to uncover the growth of social media activism.

Foucault Welles, an associate professor at Northeastern, says that network science “lets us distill vast, chaotic online communication data down to its essence” and “pull out important themes, people, and events for close reading.” This intersection with big data is critical: that it can extract patterns from terabytes of social media interactions strengthens the reach of its conclusions—the findings aren’t about how a small set of users behave, but about aggregate behavior.

The approaches highlighted in #Hashtag Activism can reveal 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 discovered that talking about sexual assault online is really powerful because it reduces stigma and encourages other people to disclose. The first few people to come forward have to be really brave and talk about what happened to them, even though they might not be believed, they might not be supported, and they might be blamed. But each time someone is brave and comes forward, it reduces the risk for other people to come forward.”
The work of Foucault Welles and colleagues provides part of a blueprint for how to construct hashtag movements moving forward. “In any given social justice movement,” she says, “there's a committed core of activists who work really hard to craft and spread a message. Then there's a huge periphery of allies and supporters who amplify that message. I love this finding because it shows how activists and regular people can work hand in hand—how we have to work hand in hand to keep things going.”

While social movements have recently come into network science’s crosshairs, the field has long focused on epidemiology. It takes little imagination to consider how a science dedicated to understanding how connections between people is important in infectious diseases. Network science has driven a large number of breakthroughs in epidemiology, from identifying the role of airline transportation in the global spread of epidemics, to revealing how the replacement of sick workers with healthy ones can drive the dynamics of influenza.

The dynamics of Covid-19 have proven especially challenging to understand, as questions have persisted about the importance of asymptomatic transmission and superspreading events. Network perspective has added layers to how we consider basic aspects of an epidemic, such as the basic reproduction number (the R0), a signature of contagiousness. The study of networks highlights that this number is truly an average, and doesn’t consider how select individuals embedded in a network can infect others in numbers much larger than predicted by the R0.

Dina Mistry, a postdoctoral fellow at the Institute for Disease Modeling, has conducted cutting-edge work on human interaction networks, and social mixing patterns. That is, she builds careful and detailed simulations of how exactly people interact, to inform public health intervention patterns, all of which are highly germane to the Covid-19 pandemic.

“We don’t know how to model contact patterns, especially in metro areas, and households,” says Mistry. Work like this is central to conversations about contact tracing, the safe reopening of schools, and other central conversations that have arisen during the Covid-19 pandemic. Mistry further suggests it's important "to collect and report on distributions of data, rather than point estimates. For example, if we think that way then maybe we can explore heterogeneity in things like behavior adoption—I want to know more than just the percent of people adopting a behavior, rather what's the distribution of willingness to adopt behaviors, for example, mask wearing, and the covariates that go with it."

Network science and our perilous future

The cases of both Foucault Welles and Mistry demonstrate network science’s fungibility, and the importance of integrating theory with data science, which aid in their ability to describe large, complicated patterns. But the true measure of a field is in what it offers for the future.

Mistry suggests that “network science has the opportunity to separate itself by focusing on what kinds of data we need.” For example, network science highlights the need for information on how households and workplaces are constructed, as these structures are the engines for transmission. Recent studies have supported this, demonstrating that phenomena like crowding can shape epidemics in cities.

Foucalt Welles hopes that network science can be used to further inform social change in academia: “Is it hard to break down silos, revisit exclusionary practices, and reconsider the foundation of how we run academia? Sure, but network scientists are great at solving complicated interdisciplinary problems. If we can figure out how people make friends, how diseases spread, what makes economies work, and where cancer comes from, I'm confident we can figure out how to set up a more just and inclusive academia too.”

Network science’s ability to connect disciplines might be its greatest attribute. With this comes the ability to adapt and respond to the state of the world, and not only sound intelligent, but deliver tangible insight, even solutions.

In a modern social order that runs on bonds between people and is undermined by corrosive ideologies that drive wedges between them, the science of how we are connected has become a natural language for the world.

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