The Dynamic Interplay of Branded Hashtag Adoption, Social Media Ties, and User Interest

This research investigates the dynamic interplay among branded hashtag adoption, social media ties, and user interest. Hashtags have become an important tool for helping marketers build a virtual community around a brand and encourage user-generated content (UGC), enhancing value for community members. We propose a novel conceptual framework grounded in identity signaling and optimal distinctiveness theory that captures the bidirectional, co-evolutionary mechanisms underlying the development of new brand communities. Using large-scale longitudinal observation data based on users’ tweets on X, we apply the Bayesian vector autoregressive method to test our hypotheses. The results reveal a negative feedback loop between user topical interest and branded hashtag adoption, where high interest increases hashtag posting propensity but subsequently reduces that same interest—potentially decreasing motivation to participate in competing brand communities. Conversely, we find a positive feedback loop between user topical interest and new social media ties that reinforces identity-consistent behavior. The study establishes dual-nature variables in brand community research, extends optimal distinctiveness theory to consumer brand-signaling behavior, and develops novel metrics based on user-generated content for predicting brand community membership decisions. Our results also offer strategic insights for managing brand communities on social media.