Stephen Prochaska

David Farr, Lynnette Hui Xian Ng, Stephen Prochaska et al.

Disinformation campaigns can distort public perception and destabilize institutions. Understanding how different populations respond to information is crucial for designing effective interventions, yet real-world experimentation is impractical and ethically challenging. To address this, we develop an agent-based simulation using Large Language Models (LLMs) to model responses to misinformation. We construct agent personas spanning five professions and three mental schemas, and evaluate their reactions to news headlines. Our findings show that LLM-generated agents align closely with ground-truth labels and human predictions, supporting their use as proxies for studying information responses. We also find that mental schemas, more than professional background, influence how agents interpret misinformation. This work provides a validation of LLMs to be used as agents in an agent-based model of an information network for analyzing trust, polarization, and susceptibility to deceptive content in complex social systems.

Sukrit Venkatagiri, Joseph S. Schafer, Stephen Prochaska

Mis- and disinformation can spread rapidly on video-sharing platforms (VSPs). Despite the growing use of VSPs, there has not been a proportional increase in our ability to understand this medium and the messages conveyed through it. In this work, we draw on our prior experiences to outline three core challenges faced in studying VSPs in high-stakes and fast-paced settings: (1) navigating the unique affordances of VSPs, (2) understanding VSP content and determining its authenticity, and (3) novel user behaviors on VSPs for spreading misinformation. By highlighting these challenges, we hope that researchers can reflect on how to adapt existing research methods and tools to these new contexts, or develop entirely new ones.

David Farr, Stephen Prochaska, Jack Moody et al.

Comprehending the information environment (IE) during crisis events is challenging due to the rapid change and abstract nature of the domain. Many approaches focus on snapshots via classification methods or network approaches to describe the IE in crisis, ignoring the temporal nature of how information changed over time. This work presents a system-oriented framework for modeling emerging narratives as temporally evolving semantic structures without requiring prior label specification. By integrating semantic embeddings, density-based clustering, and rolling temporal linkage, the framework represents narratives as persistent yet adaptive entities within a shared semantic space. We apply the methodology to a real-world crisis event and evaluate system behavior through stratified cluster validation and temporal lifecycle analysis. Results demonstrate high cluster coherence and reveal heterogeneous narrative lifecycles characterized by both transient fragments and stable narrative anchors. We ground our approach in situational awareness theory, supporting perception and comprehension of the IE by transforming unstructured social media streams into interpretable, temporally structured representations. The resulting system provides a methodology for monitoring and decision support in dynamic information environments.