Date of Award

3-2025

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Operational Sciences

First Advisor

Phillip M. LaCasse, PhD

Abstract

This study introduces a novel content-driven influence measurement framework, built around a custom influence formula that integrates Non-negative Matrix Factorization (NMF) topic modeling, sentiment analysis, and influence metrics to analyze media narratives over time. Applied to news coverage of the 2020 U.S. presidential election and the COVID-19 pandemic, the framework identifies key topics, sentiment patterns, and influential sources. Results demonstrate its ability to distinguish between transient political controversies and sustained public health discourse while capturing shifts in media influence. While effective, refinements in topic separation, sentiment analysis, and temporal weighting could enhance adaptability. This study highlights the novel influence formula as a key contribution to computational media analysis, providing a structured approach to assessing news dissemination and agenda-setting.

AFIT Designator

AFIT-ENS-MS-25-M-171

Comments

An embargo was observed for posting this work.

Distribution Statement A: Distribution Unlimited. Approved for public release. PA case number: 8ABW-2025-0305

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