Cohesion and Coordination for Strategic Collective Action in Content Recommendation

Increasingly, algorithmic systems are mediating which content users are recommended, thus shaping their exposure to creative media. As awareness of this curation process increases, users have begun to organize deliberate, strategic responses in an attempt to influence which content is promoted online. Existing theoretical models of this behavior, spanning both adversarial “attack” and, more recently, algorithmic collective action literature often represent participating users as having no prior histories and adopting a single unified strategy, effectively behaving as a squadron of clones. We argue that this abstraction overlooks two properties central to real human collectives: cohesion, the similarity of members before collective action begins, and coordination, the extent to which members align their behavior during strategic action. We introduce a novel model of collective action that parameterizes cohesion and coordination as continuous, independently controllable quantities. We derive conditions characterizing when a collective's strategic behavior shifts a target item's representation and increases its average predicted relevance, recovering the traditional unified setting as a special case while extending the analysis to partially cohesive and partially coordinated collectives. Controlled synthetic experiments validate these findings and show that successful algorithmic collective action does not require users to behave as clones and, more broadly, that the internal structure of a collective can fundamentally shape its ability to influence algorithmic curation.