The emergence of "Fake News" and misinformation via online news and social media has spurred an interest in computational tools to combat this phenomenon. In this paper we present a new "Related Fact Checks" service, which can help a reader critically evaluate an article and make a judgment on its veracity by bringing up fact checks that are relevant to the article. We describe the core technical problems that need to be solved in building a "Related Fact Checks" service, and present results from an evaluation of an implementation.
Most previous work in Information Retrieval is based on features corresponding to the frequency of occurrence of terms n-grams in a document and in the corpus. Kurland and Lee @cite_33 introduced the idea of using the structure of the corpus to help with ad-hoc queries. In their work, they used clustering to identify the structure in the corpus. Long-running themes are one kind of structure that may occur in corpora of news articles. Here, we use Topic Modeling for identifying these themes, and then use these themes to retrieve relevant fact checks. We believe that this approach has wide applicability in Information Retrieval, beyond fake news and fact checks.
@cite_33
2027445772
Most previous work on the recently developed language-modeling approach to information retrieval focuses on document-specific characteristics, and therefore does not take into account the structure of the surrounding corpus. We propose a novel algorithmic framework in which information provided by document-based language models is enhanced by the incorporation of information drawn from clusters of similar documents. Using this framework, we develop a suite of new algorithms. Even the simplest typically outperforms the standard language-modeling approach in precision and recall, and our new interpolation algorithm posts statistically significant improvements for both metrics over all three corpora tested.