Check_square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features
This work addresses the problem of automating fact-checking for social media content to combat fake news, representing an incremental improvement in domain-specific methods.
The paper tackled claim detection in social media by developing models for check-worthiness prediction and claim retrieval, achieving best-performing results for English and Arabic tweets through fusion of transformer and syntactic features.
In this digital age of news consumption, a news reader has the ability to react, express and share opinions with others in a highly interactive and fast manner. As a consequence, fake news has made its way into our daily life because of very limited capacity to verify news on the Internet by large companies as well as individuals. In this paper, we focus on solving two problems which are part of the fact-checking ecosystem that can help to automate fact-checking of claims in an ever increasing stream of content on social media. For the first problem, claim check-worthiness prediction, we explore the fusion of syntactic features and deep transformer Bidirectional Encoder Representations from Transformers (BERT) embeddings, to classify check-worthiness of a tweet, i.e. whether it includes a claim or not. We conduct a detailed feature analysis and present our best performing models for English and Arabic tweets. For the second problem, claim retrieval, we explore the 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 query tweet.