This will be a hybrid in-person and online talk.
Social media relies on amplification. It is at the heart of how marginalized communities voice injustices, how information operations stoke long-standing racial divisions, how elected officials communicate public health guidance, how misinformation proliferates through vulnerable populations, how unlikely friends connect online, and how abusers perpetrate harassment at scale. In all of its uses, good and bad, amplification is critical to how individuals construct online communication networks. These networks emerge from the ties that are implied by amplification: any share, retweet, or crosspost by one person forms a connection between them and the person that they are amplifying. As people amplify many different memes, stories, videos, and other content, they gradually create a network with a dense core, consisting of all those who received the most amplification. Around that core radiates a periphery, all those who amplified content from the core, giving it visibility. These two network components are interdependent: without the periphery’s amplification, there is no core; and without the core’s content, there is no periphery. So it is only by considering these two components together—the core and the periphery—that we can fully understand the structure of amplification.
Here, I detail the network structure of online amplification through three research projects. I start by first explicating what it means to represent social media connections as networks. Accounting for the endless variety of possible nodes and edges, I develop a minimal but expressive framework for describing social media networks and the choices that we make when constructing them. This establishes a consistent vocabulary for articulating the components of social media networks and the different types of networks they can form. Using this framework as a foundation, I narrow my focus to amplification networks and their core-periphery structure. To describe that structure in full, I present a general typology of core-periphery structure, showing that there are multiple ways of envisioning how core and peripheral nodes connect to one another. I formulate these qualitative characterizations as distinct statistical models, and show how core-periphery model selection affects our measurement of amplification. Bringing the previous two projects together, I finish by examining polarized amplification networks that emerge around divisive focal events on Twitter. I distinguish between two different forms of amplification—direct and remix amplification—and show that right-leaning publics use remix amplification disproportionately more than those on the left, creating networks that are less structurally polarized than those produced by direct amplification alone. However, the structural cohesiveness of these networks comes at the cost of consistent hostility from the right, challenging deliberative theories of the public sphere that assert more discursive connections are generally desirable. By unifying network science with communication theory to describe online amplification, I advance our understanding of how ideas, beliefs, and stories reverberate through the networked public sphere.
Brooke Foucault Welles (Chair), Network Science Institute, Northeastern University
David Lazer, Network Science Institute, Northeastern University
David Smith, Khoury College of Computer Sciences, Northeastern University
Sandra González-Bailón, Annenberg School for Communication, University of Pennsylvania
Ryan is a fifth-year Ph.D. candidate working with Professor Brooke Foucault Welles as a part of the Communication Media and Marginalization (CoMM) Lab. At the Network Science Institute, he studies online amplification and its effects on hashtag activism, misinformation, and polarization. To do so, his research makes advances in network science and text-as-data methodology for measuring the complexities of social media. Ryan holds an MS in mathematics from the University of Vermont, where he worked with the Computational Story Lab at the Vermont Complex Systems Center, and a BA in mathematics from the University of Connecticut.