Given this class’s goal of approaching and exploring the spaces where algorithms and the production, reception, and repurposing of cultural texts meet, we would like to explore the intersection between the various valences of legibility and oppositional reading strategies in the consumption of popular media texts, and the limits and possibilities of making these vital human uses of media legible to computational processes. Given that one of the largest obstacles to “reading” the affect or emotional valences of social media texts is understanding inflections of irony on the part of the author, we intend to explore this apparent limitation as it functions in relation to another type of reading – the cluster of different viewing practices and strategies which might generally be termed “ironic.” What tools might we be able to create to make legible to algorithmic processing occurrences of ironic enjoyment or engagement with a given cultural text, in opposition to what might be termed “innocent,” “straightforward,” or even “uncritical” enjoyment? In her exploration of the divide between such viewer positions in relation to the film Disco Dancer, Neepa Majumdar conceives of this divide as “queer” vs. “straight” reading positions. These more general terms can be broken down into practices of camp, irony, and oppositionality, all of which raise critical questions of politics, class, taste, and cultural capital which are deeply ingrained in the act of media reception.
In exploring these multiple layers of language and legibility (aesthetic, political, technological), we hope to generate interesting questions about both human and computational “reading” by thinking critically about the limits of algorithmic potential to make legible human strategies of cultural use (and even “making do,” to cite de Certeau) in which pleasure, desire, and affect are so deeply entwined. We will be using a data-set of tweets collected through R-Shief on a specific cultural text which invites both ironic and straight viewing practice and positions, which are also either recent or popular enough to be able to generate a large enough data-set (for example, shows like Ancient Aliens, Vanderpump Rules, or even Alex Jones’ Infowars, or franchises like The Fast and the Furious). By adapting pre-existing open-source tools built to examine text and determine an emotional or affective reading of the author, we hope to propose strategies which attempt to make possible (or legible) gaining an understanding of the author’s underlying reading positionality or postures. By thinking critically along these boundaries, we hope to expand our understanding of both the computational limits and possibilities of legibility, as well as the function of such cultural reading strategies and practices as they intersect with the specificities of social media platforms.