In this work, we introduce MultiReflect, a novel multimodal self-reflective Retrieval Augmented Generation (RAG)-based automated fact-checking pipeline. MultiReflect is designed to address the challenges of rapidly outdated information, limitations in human query capabilities, and expert knowledge barriers in fact-checking. Our proposed pipeline leverages the latest advancements in Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) to enhance fact verification across text and images. Specifically, by integrating multimodal data processing with RAG-based evidence reflection, our system improves the accuracy of fact-checking by utilizing internet-sourced verification. We evaluate our results on the VERITE benchmarks and using several multimodal LLMs, outperforming baselines in binary classification.

Newspapers have always remained an important medium for disseminating information to the masses. With continuous access and availability of news, there is a severe competition among news media agencies to attract user attention. Therefore, ensuring fairness in news reporting, such as, politically stance neutral reporting has become more crucial than before. Although several research studies have explored and detected political stance in English news articles, there is a lack of research focusing on low-resource languages like Estonian. To address this gap, this paper examines the effectiveness of established stance-detection features that have been successful for English news media, while also proposing novel features tailored specifically for Estonian. Our study consists of 32 different features comprising of lexical, Estonian-specific, framing and sentiment-related features out of which we identify 15 features as useful for stance detection.