A new article on “Visual Models for Social Media Image Analysis: Groupings, Engagement, Trends, and Rankings” co-authored by Public Data Lab researchers Gabriele Colombo, Liliana Bounegru and Jonathan Gray has just been published in the International Journal of Communication (IJOC). It is available as an open access PDF. Here’s the abstract:
With social media image analysis, one collects and interprets online images for the study of topical affairs. This analytical undertaking requires formats for displaying collections of images that enable their inspection. First, we discuss features of social media images to make a case for studying them in groups (rather than individually): multiplicity, circulation, modification, networkedness, and platform specificity. In all, these offer reasons and means for an approach to social media image research that privileges the collection of images as its analytical object. Second, taking the 2019 Amazon rainforest fires as a case study, we present four visual models for analyzing collections of social media images. Each visual model matches a distinctive spatial arrangement with a type of analysis: grouping images by theme with clusters, surfacing dominant images and their engagement with treemaps, following image trends with plots, and comparing image rankings across platforms with grids.