The First Mass Protest on Threads: Multimodal Mobilization and AI-Generated Visuals in Taiwan’s Bluebird Movement

Abstract

The 2024 Bluebird Movement in Taiwan marked one of the largest youth-led protests in the country’s democratic history, mobilizing over 100,000 demonstrators in response to parliamentary reforms. Unlike the 2014 Sunflower Movement, Bluebird unfolded within a transformed digital environment dominated by Threads, Meta’s new microblogging platform that draws 24% of its global traffic from Taiwan. Leveraging a dataset of 62,321 posts and 21,572 images, this study analyzes how protest communication developed across textual and visual modalities. We combine LLM zero-shot annotation, gradient-boosting trees, and SHAP explainers, to disambiguate the supply and demand of attention. Results reveal three dynamics: (1) a partisan gap between algorithmic exposure and user engagement, with anti-DPP content surfaced more widely but anti-KMT and pro-DPP content more actively recirculated; (2) textual repertoires centered on commemorations, personal testimonies, and calls to action as key drivers of virality; and (3) a bifurcation in visual strategies, where human photographs concentrated exposure and discussion, while AI-generated animal symbols circulated as mobilization tools and partisan attacks. The exposure-engagement gap demonstrates how Threads functions as both amplifier and filter of democratic contention, suggesting dual publics operating under distinct logics. Generative AI further reshapes symbolic repertoires through kawaii toxicity, or partisan attacks cloaked in cute aesthetics.