Kicking politics: Towards a framework for detecting political influence in online communities

Political influence on social media is not confined to explicitly political spaces. We examine how UK football fan communities on Twitter became conduits for political messaging in the post-Brexit Referendum period (2016–2017). Although political tweets represented fewer than 0.1% of 152 million football-related posts, they achieved substantial amplification through network position, identity alignment, and platform dynamics. Using a hybrid approach that integrates social network analysis with iterative content filtering, we isolate 94,846 political tweets and map how political narratives entered and propagated within fan networks. We propose an analytical framework that links recurring mechanisms of political influence to observable network and content signatures, enabling the systematic identification of candidate regions for closer investigation. We identify six distinct influence mechanisms: hashtag hijacking, suspected coordinated inauthentic amplification, hybrid activism embedded within fan groups, elite political “megaphones”, media provocation and inadvertent amplification. Five case studies collectively illustrate these six mechanisms. Each mechanism exhibits characteristic network signatures, including star-like amplification hubs, tightly reciprocal clusters, and dense ideologically aligned user communities that facilitate the diffusion of political narratives. Our findings show that political messaging was not randomly dispersed but concentrated within connective nodes of the football discourse network, enabling cross-community transmission. Propagation and amplification of political messages depended less on message volume than on structural integration. Beyond documenting a historical episode, we contribute a transferable analytical pipeline and a reproducible framework for identifying candidate regions of political influence through integrated network and content analysis, providing a foundation for future automated detection approaches.