Improved Methods to Aid Unsupervised Evidence-Based Fact Checking for Online Heath News

Abstract

False information in the domain of online health related articles is of great concern, which has been witnessed abundantly in the current pandemic situation of Covid-19. Recent advancements in the field of Machine Learning and Natural Language Processing can be leveraged to aid people in distinguishing false information from the truth in the domain of online health articles. Whilst there has been substantial progress in this space over the years, research in this area has mainly focused on the sphere of political news. Health fake news is markedly different from fake news in the political context as health information should be evaluated against the most recent and reliable medical resources such as scholarly repositories. However, one of the challenges with such an approach is the retrieval of the pertinent resources. In this work, we formulate two techniques for the retrieval of the most relevant authoritative and reliable medical content from scholarly repositories which can be used to assess veracity of an online health article. The first technique is an unsupervised method of generating queries from claims which are extracted from an online health article. We propose a three-step approach for it and illustrate that our method is able to generate effective queries which can be used for retrieval of information from medical knowledge databases. The second method involves a filtering approach for extracting the most relevant information for the claims. We show how this can be achieved with the help of state of the art transformer models and illustrate it's effectiveness over other methods.