Online social networks produce a visuality that reflects the attention economy governing this space. What is seen becomes elevated into prominence by networked publics that ‘perform’ affective expressions within platform affordances. We mapped Twitter images of refugees in two language spaces – English and Arabic. Using automated analysis and qualitative visual analysis, we found similar images circulating both spaces. However, photographs generating higher retweet counts were distinct. This highlights the impact of affective affordances of Twitter – in this case retweeting – on regimes of visibility in disparate spheres. Representations of refugees in the English language space were characterized by personalized, positive imagery, emphasizing solidarity for refugees contributing to their host country or stipulating innocence. Resonating images in the Arabic space were less personalized and depicted a more localized visuality of life in refugee camps, with an emphasis on living conditions in refugee camps and the efforts of aid organizations.
Honeycomb: Visual Analysis of Large Scale Social Networks
