QAnon’s Hashtag Hijacking Strategies and Engagement Dynamics Across Discursive Themes on Twitter: Negative Binomial Models of Online Visibility

The digital presence of far-right movements often relies on the strategic use of hashtags. Beyond signalling in-group narratives, hashtags are frequently deployed to insert far-right content into unrelated conversations, a practice known as “hashtag hijacking.” This tactic seeks to influence online discourse by amplifying far-right narratives, subverting mainstream conversations, and manipulating platform visibility. Nevertheless, questions remain regarding its effectiveness in generating user engagement. This study examines QAnon-related content on Twitter between January 2020 and January 2021, a period marked by heightened activity surrounding the movement. Using chi-squared tests, Negative Binomial Generalized Linear Models (NB-GLM), and Latent Dirichlet Allocation (LDA), the analysis investigates the prevalence of hashtag hijacking, its relationship to engagement, and how its effects vary across thematic contexts. The results identify two primary forms of hijacking—contextless hashtags and news-related hashtags—and show that hijacking alone did not significantly increase engagement. However, when paired with specific narratives, such as the 2020 U.S. elections, hijacked hashtags achieved greater reach. These findings advance understanding of how far-right groups exploit platform affordances to extend discursive visibility. This underscores the need for further comparative research on the differential use of hashtags across online communities. • Analyzes QAnon hashtag hijacking on Twitter, 2020–2021 • Employs chi 2 , NB-GLM, and LDA to assess engagement effects • Identifies contextless and news-related forms of hijacking • Finds engagement rises when hijacking aligns with key narratives • Advances understanding of far-right use of platform affordances