From basic actions to subversive affordances: Development and validation of the social media algorithmic resistance scale (SM-ARS)

As social media platforms increasingly govern civic visibility and information access through opaque, NLP-driven algorithms, the power dynamic between users and technological infrastructures has become highly asymmetric. In response, user behavior has evolved from episodic platform rejection to complex, continuous practices of everyday digital resistance. However, existing research relies heavily on binary models of adoption versus discontinuance and lacks empirical instruments to capture how users reclaim agency post-adoption. To address this gap, this paper conceptualizes, develops, and validates the Social Media Algorithmic Resistance Scale (SM-ARS). Following a rigorous scale development paradigm, an exploratory phase (Study 1: N = 303) identified a multidimensional structure comprising three distinct behavioral repertoires: avoidant resistance (AR), which involves limiting structural exposure; obfuscatory resistance (OR), which involves strategic data pollution to subvert profiling; and counter-curative resistance (CCR), which involves redirected algorithmic curation and algorithmic contestation. A subsequent confirmatory phase (Study 2: N = 611) validated this structure, demonstrating strong model fit and nomological validity through significant associations with algorithm awareness and perceived algorithm responsiveness (PAR). Theoretically, the SM-ARS innovates affordance theory by introducing subversive affordances, demonstrating how respondents may strategically repurpose engagement mechanisms to disrupt platform dataveillance. Practically, this instrument provides an initial diagnostic tool for examining how social media users negotiate algorithmic power and protect their digital autonomy. However, its applicability beyond the Romanian context requires further cross-cultural validation.