Analyzing Online Structures of Attention
Date:
Abstract of Talk: When we pay attention to content online, we generate measurable trace data that provides feedback to content creators, algorithmic/AI actors, and corporations. This information flow governs what topics are discussed, how far content reaches, and what kinds of governance and moderation decisions companies like Meta or Google make to optimize their advertising revenue. The sheer complexity of attention dynamics online can make it difficult to disentangle the impact an individual user or set of users can have on structures of attention within a given online platform. Using theoretical underpinnings from network science, I provide evidence that individual users can durably reshape attention on Twitter (now known as X) through amplification of novel content to their followers (i.e. retweeting/reposting). In this talk, I will begin by introducing some basic network science concepts that are relevant to this research. I will then explain the data and methods I used to analyze following patterns on Twitter/X, walk through the results of my analyses, and discuss the broader implications my research has for the study of social networks, political polarization, and offline consequences of online behavior. Finally, I will conclude by discussing future work that explores information access and quality online from a networked perspective with a focus on algorithmic and AI actors.