Information retrieval algorithms and neural ranking models to detect previously fact-checked information.

Although in the last decade several fact-checking organizations have emerged to verify misinformation, fake news has continued to proliferate, especially through social media platforms. Even though adopting improved detection strategies is of utmost importance, the fact-checking process could be optimized by verifying whether a claim has been previously fact-checked. Despite some ad-hoc information retrieval approaches having been recently proposed, the utility of modern (neural) retrieval systems have not been investigated yet. In this paper, we consider the standard two-phases retriever-reranker architecture and benchmark different state-of-the-art techniques from the information retrieval and Q&A literature. We design several experiments on a real-world Twitter dataset to analyze the efficiency and the effectiveness of the benchmark approaches. Our results show that combining standard and neural approaches is the most promising research direction to improve retrievers performance and that complex (neural) rerankers might still be efficient in practice since there is no need to process a high number of documents to improve ranking performance. • We address the problem of detecting previously fact-checked information. • Considering the information retrieval task, we define a multi-stage retriever-reranker framework. • We benchmark the state-of-the-art retriever and re-ranker architectures. • We discuss the system's operational settings unveiling its strengths and weaknesses. [ABSTRACT FROM AUTHOR]