Stampede Theory: Mapping and Treating Dangerous Misinformation at Scale

Abstract

Our information environment can be viewed as a densely-connected socio-technical ecosystem. We communicate with each other through computers. Our networks include more than the obvious technologies like VOIP or social media. Whenever we run a search, algorithms monitor our behavior -- if enough of us pick the second or third link, then that link will rise, and a self-reinforcing pattern of query and result is created. These feedback loops crowd out the different or unusual, making the accessible information environment become less diverse. At scale, this can make our socio-technical ecosystem brittle and less resilient to shocks or manipulation. This dissertations explores these issues in two parts. The first is the development of Stampede Theory, which shows how animal behavior patterns in physical environments are similar to human belief-based behavior in online environments. There are nomadic explorers, individuals or small groups dispersed over large areas. Those who flock, engage in social interaction which dramatically increases member's protection and access to resources. In stampedes, social influence becomes the dominant factor, outweighing environmental cues and creating a subjective reality. By observing these behaviors in simulated and online belief spaces (the subset of information space that is associated with opinions), it is possible to evaluate information trustworthiness, rather than evaluating the information itself. Current fact-checking practices attempt to determine the veracity of information by, for example, comparing statements to authoritative sources. The methods described in this dissertations reduces this problem to one of identifying classes of behavior in online groups as they describe their opinions about information. The second part of this research creates \textit{navigable maps} based on the text that online actors generate over time. These sequences create trajectories in belief space. Individuals using similar terms at the same time have a high social influence. Agents whose textual trajectory aligns poorly with other agents have a low social influence and are nomads. Flocking lies between these extremes. Each of these behaviors has an identifiable signature that can be detected by comparing the trajectories. These trajectories can be woven together to create maps of belief space. The bounds of this space are defined by nomads. They explore widely, and arrive at their beliefs from different directions. Flocking behavior occurs within these bounds, and shows as popular regions with diffuse edges. Stampedes cut through these areas in narrow ridges. As such maps can be used to find and understand the relationships between trustworthy (nomad), safe (flocking), while alerting users and information systems of dangerous (stampede) belief regions.