A MIXED-METHODS LONGITUDINAL ANALYSIS OF DIFFERENCES IN FRAMING AND THE IMPACT ON U.S. NEWSPAPER COVERAGE OF SAUDI VISION 2030 (2016–2024)

Abstract

Saudi Arabia’s Vision 2030 is a comprehensive reform initiative aimed at diversifying the national economy and reshaping the Kingdom’s global image. Central to this transformation are investments in tourism, sports, and entertainment—sectors historically underrepresented in Western media narratives that have often emphasized political and security issues. Given the influential role of elite journalism in shaping international perceptions, understanding how these cultural reforms are framed in U.S. newspapers is critical. This dissertation examines coverage of Saudi Vision 2030 in The Washington Post and The Wall Street Journal from April 2016 to December 2024. Grounded in framing theory, the study employs a mixed-methods longitudinal design integrating human-coded content analysis, statistical modeling in SPSS, computational visualizations in Python, and Artificial Intelligence–assisted classification using Large Language Models (LLMs). The research evaluates dominant frames across time and compares human and algorithmic coding patterns. Findings indicate that both newspapers most frequently employed Consequences and Conflict frames. However, The Washington Post more often emphasized morality and responsibility dimensions, whereas The Wall Street Journal foregrounded economic modernization and cooperation. Although differences were observable, framing patterns remained broadly comparable. By integrating human and computational approaches, this study advances framing research in non-Western modernization contexts and contributes to methodological debates on the reliability and implications of AI-assisted media analysis.