“Image and Affect” is an open-source, collaborative browser plugin tool that enables users to record their affective states while encountering images on their social media feeds. This exploratory approach responds to the immense importance of visual information in relation to affect production and circulation that is otherwise overlooked by strictly textual approaches to content on the internet. Whether in the form of memes, infographics or photographs, images stand to have a greater impact on users’ affective states, transferring information while also producing potentially visceral responses during otherwise innocuous everyday browsing. By developing a tool that speaks directly to the significance of affectively charged images, we propose a conception of both affects and users that is the opposite of the kind of sentiment analysis and extractivism that dominates corporate social media platforms. Specifically, the tool makes it possible for users to understand affect as layered, ambiguous and changing, rather than dealing with systems that either pin down or provoke an affective response to produce personalized recommendations or shape responses and behaviours. As they use the tool, users discover that multiple and contradictory affects can coexist, and that affects transform through time. As well, the group discussion taking place after using the tool also directs users to the images they did not affectively tag, opening the door to further understanding digital lethargy (Hu, 2022) and disaffection. Potential applications of Image and Affect include participatory, public facing workshops that will render collected emotional data in diverse, experimental formats.
Ganaele researches digital cultures, both new (e.g. mis/disinformation, algorithmic biases) and old (textile weaving). She is currently part of the Data Fluencies project hosted at the Digital Democracies Institute at Simon Fraser University.