Date of Award
Summer 5-19-2026
Document Type
Thesis
Publication Status
Version of Record
Submission Date
July 2026
Department
Information Technology and Operations Management
College Granting Degree
College of Business
Department Granting Degree
Information Technology and Operations Management
Degree Name
Master of Science (MS)
Thesis/Dissertation Advisor [Chair]
Nataliia Neshenko
Abstract
Interpreting cybersecurity incidents within critical infrastructure requires analysis across a complex information environment. Authoritative reporting, open web journalism, and Al-generated summaries coexist but often diverge. This study examines narrative properties across these three environments. Lexical uncertainty analysis, embedding-based semantic drift measurement, and probabilistic topic modeling are applied to 908 human-produced documents covering five U.S. critical infrastructure incidents. Results indicate that narrative divergence varies systematically by incident rather than by source type. Factors such as technical complexity, attribution ambiguity, and public visibility influence this divergence. Preliminary analysis of 429 Al-generated narratives indicates that large language models create a distinct environment, characterized by lower uncertainty, measurable divergence from authoritative baselines, and a thematic focus on public-facing content rather than technical detail. The source consulted first may shape an individual's preliminary understanding of the incident. This has potential implications for threat perception and attribution framing as AI systems become more widely used.
Recommended Citation
Marquez, Marco, "NARRATIVE DIVERGENCE ACROSS INFORMATION ENVIRONMENTS: CRITICAL INFRASTRUCTURE CYBERSECURITY REPORTING AND AI-GENERATED NARRATIVES" (2026). Electronic Theses and Dissertations. 385.
https://digitalcommons.fau.edu/etd_general/385