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

2026-02-02

AI Summary

This study investigates how the Threads platform facilitated youth protest mobilization during Taiwan’s 2024 Bluebird Movement through textual and visual content, revealing tensions between algorithmic recommendation and user-driven dissemination. Drawing on a multimodal dataset of 62,321 posts and 21,572 images, the research employs zero-shot large language model annotation, gradient-boosted trees, and SHAP interpretability to conduct multimodal attribution analysis. It introduces the concept of “kawaii toxicity” to explain how generative AI reconfigures movement symbolism via cute animal and plant motifs. The findings indicate that anti-DPP (Democratic Progressive Party) content receives greater algorithmic visibility, whereas anti-KMT (Kuomintang) and pro-DPP messages are more likely to be actively shared by users. Furthermore, authentic human photographs dominate engagement metrics, while AI-generated images serve primarily as mobilization tools and vehicles for political critique.

Technology Category

• Humans and AI: Voting
• Computer Vision: Multi-modal Vision
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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$\unicode{x2013}$uniquely$\unicode{x2013}$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) partisan asymmetries between algorithmic exposure and user endorsement, 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 and plant symbols circulated as mobilization tools and partisan attacks. These findings demonstrate how Threads functioned as both an amplifier and filter of democratic contention, extending theories of emotional and visual contagion by showing how generative AI reshapes symbolic repertoires in contemporary protest through what we term kawaii toxicity$\unicode{x2013}$political attacks cloaked in aesthetics of cuteness.

Problem

• protest mobilization
• AI-generated visuals
• social media
• multimodal communication
• political contention

Innovation

• generative AI
• multimodal mobilization
• kawaii toxicity
• Threads platform
• visual contagion