Studying images in social media poses specific methodological challenges, which in turn have directed scholarly attention toward the computational interpretation of visual data. When analyzing large numbers of images, both traditional content analysis as well as cultural analytics have proven valuable. However, these techniques do not take into account the contextualization of images within a socio-technical environment. As the meaning of social media images is co-created by online publics, bound through networked practices, these visuals should be analyzed on the level of their networked contextualization. Although machine vision is increasingly adept at recognizing faces and features, its performance in grasping the meaning of social media images remains limited. Combining automated analyses of images with platform data opens up the possibility to study images in the context of their resonance within and across online discursive spaces. This article explores the capacities of hashtags and retweet counts to complement the automated assessment of social media images, doing justice to both the visual elements of an image and the contextual elements encoded through the hashtag practices of networked publics.

Studies into affective publics often involve textual communication. However, emotive communication is increasingly visual. This study zooms in on the representation of the suffering other in seven re-workings of the Alan Kurdi photographs that resonated significantly on Instagram. Chouliaraki’s concept of post-humanitarian solidarity in The Ironic Spectator (2013) is used as a theoretical framework to analyse the content of re-worked images and their post captions. Her concept outlines how distant sufferers tend to be rendered invisible due to the self-reflexive nature of contemporary solidarity. This self-reflexivity gets in the way of solidarity for others unlike us. The study found that, although the sufferer is visually present in almost all re-worked images, the suffering is ‘replaced’ by emotions or political views of the creators. Both Chouliaraki’s ‘distant other’ as well as Markham’s similar other are ways to visually (re)construct the tragedy of Alan Kurdi and the refugee crisis in general. This study adds to this an understanding of how Instagram users, while visually constructing a similar or distant other, also write themselves – often their personal feelings – into such images. Their public, other Instagram users, engages in self-reflexivity by liking such re-workings, aligning with the communicated emotions or political views conveyed. In this way, the platform ‘like feature’ intensifies the self-reflexive nature of contemporary solidarity.

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.

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Focusing on the (early) run-up to and aftermath of the 2020 U.S. presidential elections, this study examines the extent of problematic information in the most engaged-with content and with the most active users in “political Twitter.” It was found that mainstream sources are shared more often than problematic ones, but their percentage was much higher prior to the Capitol riots of January 2021. Significantly, (hyper) partisan sources are close to half of all sources shared, implying a robust presence. By March 2021, though, both the share of problematic and of (hyper)partisan sources decreased significantly, suggesting the impact of Twitter’s deplatforming actions. Additionally, active, problematic users (fake profiles, etc.) were found across the political spectrum, albeit more abundantly on the conservative side.

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