This paper presents a taxonomy for analyzing multilingual multimodal misinformation on social media, built from a large-scale seven-language Twitter/X dataset and grounded in qualitative analysis, then operationalized through an automated multi-step annotation pipeline using a vision-language model with human validation. The authors report previously unknown patterns, including a prevalence of AI-generated content in technology misinformation and news-image reuse in vaccination falsehoods.
