MMMMM: A Unified Taxonomy for Investigating the Mechanisms of Multilingual MultiModal Misinformation

Research indicates that multilingual and multimodal misinformation on social media platforms is widespread, impactful, and detrimental, yet remains challenging to identify, counter, and comprehend compared to text-based misinformation. Current understanding is hampered by insufficient taxonomies rooted in real-world contexts and the limitations of existing multimodal machine learning models, which impede large-scale automated analysis. A new research initiative aims to address these gaps by developing a comprehensive taxonomy for multimodal misinformation based on a large dataset of real-world instances from social media across seven languages.

The proliferation of sophisticated, multilingual, and multimodal misinformation poses a significant threat to information integrity, public discourse, and decision-making across various sectors. Understanding its mechanisms and developing robust detection methods are critical for maintaining trust in information ecosystems and mitigating societal and institutional risks.

Multilingual and multimodal misinformation on social media platforms is prevalent, potent, and harmful.

Forward consideration, not a verified fact.