Algorithmic iconography: Intersections between iconography and social media image research
Iconography is a qualitative methodology originating in the history of art. There is, however, a gap in its use in that we rarely know what people actually thought when they viewed specific imagery. In contrast, standard studies of social media imagery concentrate upon their placement within events or social phenomena, but commonly lack rich detail of the pictures or their history, and rarely tie social media users’ commentary to the pictures they share. This thesis develops a new methodology that brings together iconography and social media studies, filling both these gaps. The texts written by social media users provide insights into their interpretations of the pictures they share. An adapted iconographical method helps provides evidence for why the pictures they share are interpreted as they are, and why some are shared more than others.
I demonstrate iconography’s usefulness with a case study. It centres upon an historically important dataset of the 1,000 most-shared tweets containing pictures of Alan Kurdi (a Syrian refugee child found drowned on a Turkish beach) and other refugees in a two-week period beginning when Alan was found on 2 September 2015. Reaction to pictures of Alan shared on Twitter spilled into the real world and led to demonstrations on behalf of, and increased support for, refugees. This thesis explores the mechanisms by which these pictures crystallised popular sympathetic reactions for refugees at an important point in recent geopolitical history.
To aid in the iconographic analysis of this data, I have created a digital tool called a “datasheet” to help analyse visual and textual patterns. Working in tandem with data analytics software, the datasheet can also provide other forms of otherwise difficult-to-discern “top-down” insights. By using these tools, it is possible to uncover pictorial preferences for two main types of social media actors, waves of sharing over time, cross-cultural interpretative patterns, and long-standing interpretative themes such death-as-sleep, children as angels, oversized monumentality, standard representations of refugees, the fixing of ambiguous imagery through anchoring text, and hostile representations of refugees as threats. Many of these themes returned after the time of data collection as the political climate surrounding refugees soured in 2015–2016. The iconographic approach and the organisation of this datasheet provide a basis for future analysis of imagery shared on social media.
Research Degree: PhD, Full-time
Department: Visual Studies