Natural stories, artificial authors. synthetic resistance strategies in social reading communities

Abstract

This paper investigates how social reading communities respond to the spread of GenAI-generated texts, with particular attention to the genres of romance, dark romance, and romantasy. Through a range of case studies (NaNoWriMo; the Inkitt/Galatea ecosystem; and AI-assisted serialisation), it shows how authorship is problematised in terms of stylistic standardisation, algorithmic ghostwriting, and the redefinition of reputation.

The theoretical framework revisits the notion of algorithmic interpellation through the lens of algorithmic politics (DuBrin, Gorham), interpreting practices such as low-intensity deplatforming, anti-plagiarism “red flags”, review bombing, and coordinated reporting as forms of “resistance to synthetic interpellation”.

We argue that the fear of being mistaken for bots (Beer 2024) pushes authors to experiment with new writing canons, while simultaneously making visible the ghosts of contemporary publishing culture (Calvino 1967). In this sense, we conclude that within ecosystems in which human and non-human actors coexist, social reading can assume a political dimension that, through ethical protocols, allows synthetic literature to avoid erasing queer and BIPOC voices or further precarising editorial labour.