Reclaiming the Feed: Queer Media Education for the Platform Society

The chapter advances a Queer Media Education model oriented toward algorithmic literacy. Drawing on media-education theory and algorithmic reverse engineering (Sandvig et al., 2014), the model teaches learners to see the algorithm behind feeds and recommendations, reconstruct its input data, ranking logics, and optimization metrics, and develop strategies for self-protection and informational reclaim (Purdue University, 2023). Goals include expanding user agency by reconfiguring default settings, diversifying sources to escape filter bubbles, and even monitoring the algorithms themselves (Bull et al., 2021). The model adopts a transnational and platform-centric perspective, rather than focusing on a single geographical region. It is designed to be applicable to users worldwide, through different media and platforms, such as Instagram, Facebook and X, but also Netflix, Steam and other streaming services. While queer users across countries experience platformed discrimination differently, the algorithmic structures they interact with (content moderation, recommendation engines, shadow banning) are transnational in scope and logic. Through this lens, the visibility/invisibility dialectic becomes a pedagogical testing ground: choosing tactical opacity as self-defence or deploying strategic visibility aligns with hooks’s pedagogy of the margin (hooks, 1994). Cultivating algorithm-literate citizens is thus not merely defensive; it is the precondition for transforming recommendation engines from commercial gatekeepers into catalysts of pluralism, knowledge, and cultural innovation.