This study examines how elite figures shape polarisation on Twitter/X through the interplay of content, structure, and engagement strategy. Drawing on data from nine globally influential users (2010–2021), the research integrates natural language processing, network analysis, and causal modelling to test five hypotheses grounded in social identity, agenda-setting, and two-step flow theories. Entity co-occurrence networks reveal that polarised discourse forms denser, more clustered networks than non-polarised content, indicating tighter semantic cohesion around socially and politically charged entities. Thematic and sentiment analyses show that posts addressing non-core topics – particularly those concerning social justice, environmental sustainability, philanthropy, and global welfare – are nearly five times more likely to be polarised than core professional themes. Negative emotional tone further amplifies this effect, while higher tweet-to-retweet ratios reduce polarisation, underscoring the moderating role of original content production. A user-level mediation analysis tested whether topical diversity transmits the effect of follower scale on polarisation but found no significan
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