The University of Sheffield at CheckThat! 2020: Claim Identification and Verification on Twitter

Abstract

The spread of misinformation online has been gathering pace in recent years which has led to research into automatic methods for claim verification. The COVID-19 pandemic presents a unique challenge due to the large amount of inaccurate information being shared on social media platforms. This paper describes the University of Sheffield’s entry to the CLEF 2020 CheckThat! Lab, which focuses on the problems of determining check-worthiness and verification of claims found in tweets, including those related to COVID-19. For the Tweet Check-Worthiness Task (Task 1), we found that TF-IDF term weightings used by a Random Forest model outperformed more complex approaches employing Word2Vec embeddings and recurrent neural networks, and for the Claim Retrieval Task (Task 2), we found that BM25 similarity score weightings based on TF-IDF term weightings with a Support Vector Machine classifier scoring model outperformed other methods making use of cosine and Euclidean similarity metrics, and regression-based scoring models.