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.

Why it matters

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.

Key insights

• Multilingual and multimodal misinformation on social media platforms is prevalent, potent, and harmful.
• Detection and countering of multimodal misinformation are difficult, and its mechanisms are poorly understood compared to text-only misinformation.
• Existing research is hindered by a lack of taxonomies grounded in real-world contexts.
• Current multimodal machine learning models have limitations that prevent automated annotation and analysis at scale.
• A large-scale, high-quality dataset of real-world misinformation instances from Twitter/X in seven languages is being collected.
• A novel, comprehensive taxonomy for multimodal misinformation is being developed, grounded in an in-depth analysis.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.29681