Visual Framing in the AI Era: Lessons from Manual Approaches for Computational Methods

Abstract

Computational methods can minimize the time and resources needed to manually code thousands of images. Yet, they also come with challenges, including validation, algorithmic bias, and privacy concerns. Acknowledging that the pictorial turn has now entered a computational phase, this article reports on a manual and automated coding of 7000+ images to better understand online extremist content. Using Rodriguez and Dimitrova’s (2011) four-tiered model of visual framing, the study compares manual and OpenAI’s ChatGpt4o’s coding of Al-Qaeda and ISIS images across the denotative, semiotic, connotative, and ideological levels. AI coding exhibited moderate to strong performance on denotative variables but was weaker in the semiotic and connotative tiers. The study concludes with a discussion of the advantages of human and AI functioning together to better understand visual framing.