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In this digital age of news consumption, a news reader has the ability toreact, express and share opinions with others in a highly interactive and fastmanner. As a consequence, fake news has made its way into our daily lifebecause of very limited capacity to verify news on the Internet by largecompanies as well as individuals. In this paper, we focus on solving twoproblems which are part of the fact-checking ecosystem that can help toautomate fact-checking of claims in an ever increasing stream of content onsocial media. For the first problem, claim check-worthiness prediction, weexplore the fusion of syntactic features and deep transformer BidirectionalEncoder Representations from Transformers (BERT) embeddings, to classifycheck-worthiness of a tweet, i.e. whether it includes a claim or not. Weconduct a detailed feature analysis and present our best performing models forEnglish and Arabic tweets. For the second problem, claim retrieval, we explorethe pre-trained embeddings from a Siamese network transformer model(sentence-transformers) specifically trained for semantic textual similarity,and perform KD-search to retrieve verified claims with respect to a querytweet.

Check_square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features