COVID -19 Claim Radar: A Structured Claim Extraction and Tracking System (2022)

Abstract: To tackle the challenge of accurate and timely communication regarding the COVID-19 pandemic, we present a COVID-19 Claim Radar to automatically extract supporting and refuting claims on a daily basis. We provide a comprehensive structured view of claims, including rich claim attributes (such as claimers and claimer affiliations) and associated knowledge elements as claim semantics (such as events, relations and entities), enabling users to explore equivalent, refuting, or supporting claims with structural evidence, such as shared claimers, similar centroid events and arguments. In order to consolidate claim structures at the corpus-level, we leverage Wikidata as the hub to merge coreferential knowledge elements. The system automatically provides users a comprehensive exposure to COVID-19 related claims, their importance, and their interconnections. The system is publicly available at GitHub and DockerHub, with complete documentation.

## introduction claims present in daily news are unfiltered and potentially of great value, but can also have negative effects when misinformation is widespread. the covid-19 pandemic is a crucial example of when false claims can be particularly harmful, with the torrent of misinformation impacting public perception. for example, a claim such as "vaccines are dna changers" is likely to discourage vaccinations. ## overview the architecture of our structured claim extraction system is illustrated in figure 2 . the system pipeline consists of different components with two main modules, namely, claim extraction (ce) (section 3) and knoweldge extraction (ke) (section 4). each module creates a separate knowledge base, using the document corpus as input. the corpus-level knowledge base is then associated to claims according to the justifications, as well as coreferential entities and events. inter-claim relations such as equivalent, supporting or refuting are then identified based on their structural connections (section 5). 3 claim extraction ## claim-claim relation extraction we consolidate the claims from the entire corpus according to the wikidata qnode linking results and claim attributes. ## related work claim detection is a central task in argumentation mining @xcite @xcite @xcite . it aims to identify argument components and their relations, including contextdenpendent methods @xcite with topics as input, and context-independent methods @xcite without predefined topics. @xcite proposes corpus-wide claim detection to extend the traditional document-level setting. related work also involves claimer dectection @xcite and stance detection @xcite . covid-19 related claim detection and argument mining are generally still limited. the majority of other argument mining approaches for the biomedical domain focus on research literature @xcite @xcite . the work by @xcite is one of the few exceptions that tackle this challenge and propose a pipeline to extract health-related claims with claim attributes from news articles. however, it does not attempt associating claims and their attributes with structured knowledge elements. to the best of our knowledge, detecting structured covid-19 claims associated with structured knowledge elements has not been approached yet. our system leverages the state-of-the-art information extraction and wikidata entity linking techniques to dynamically construct a covid-19 claim knowledge base. ## conclusions and future work we present our covid-19 claim radar system, to automatically extract claims in real time from rapidly updating information on the covid-19 pandemic. we provide users with an in-depth structured view of claims, along with associated knowledge elements. our system enables exploring various inter-claim connections, including supporting and refuting relations, shared claimers and claim objects, along with related events and entities. in future work, we plan to validate claims from multiple modalities, languages, and sources, as well as support information surgery to correct false claims automatically. in addition, we aim to track claims so as to predict changes in perspectives of claimers and facilitate generating alerts for such changes.