Paper detail

Correcting misinformation on social media with a large language model

Real-world information, often multimodal, can be misinformed or potentially misleading due to factual errors, outdated claims, missing context, misinterpretation, and more. Such "misinformation" is understudied, challenging to address, and harms many social domains -- particularly on social media, where it can spread rapidly. Manual correction that identifies and explains its (in)accuracies is widely accepted but difficult to scale. While large language models (LLMs) can generate human-like language that could accelerate misinformation correction, they struggle with outdated information, hallucinations, and limited multimodal capabilities. We propose MUSE, an LLM augmented with vision-language modeling and web retrieval over relevant, credible sources to generate responses that determine whether and which part(s) of the given content can be misinformed or potentially misleading, and to explain why with grounded references. We further define a comprehensive set of rubrics to measure response quality, ranging from the accuracy of identifications and factuality of explanations to the relevance and credibility of references. Results show that MUSE consistently produces high-quality outputs across diverse social media content (e.g., modalities, domains, political leanings), including content that has not previously been fact-checked online. Overall, MUSE outperforms GPT-4 by 37% and even high-quality responses from social media users by 29%. Our work provides a general methodological and evaluative framework for correcting misinformation at scale.