Digitally-assisted iconology: A method for the analysis of digital media

Abstract

Exploring medium-to-large datasets of social media imagery can be challenging. This paper describes a digitally-assisted iconology, a hybrid methodology that includes machine learning and data analytics for sorting through medium-sized datasets of images that lack metadata to describe their pictorial content. The method plays to the strengths of current digital technologies. Using machine learning, pictures are first clustered in a preliminary stage based upon basic formal presentational characteristics. Thematic analysis follows this preliminary stage, based upon an expansion of Aby Warburg’s “pre-coined expressive values”, which are frequently found in pictures displaying high levels of user reception. Once clustered via these two separate stages, the researcher can then drill down using familiar forms of visual analysis to explore how similar concepts have been rendered in different ways. The analysis may be augmented by exploring the commentary appended to these pictures, which adds a further level of detail providing insight into end-user interpretations. The approach – including its drawbacks – is demonstrated via a consequential dataset of pictures shared on Twitter in 2015, after a Syrian child was found drowned off the Turkish shore. Derivative imagery based upon the original photographs referenced longstanding iconographic themes.