QMUL-SDS at CheckThat! 2021: Enriching pre-trained language models for the estimation of check-worthiness of Arabic tweets

This paper describes our submission to the CheckThat! Lab at CLEF 2021, where we participated in Subtask 1A (check-worthy claim detection) in Arabic. We introduce our approach to estimate the checkworthiness of tweets as a ranking task. In our approach, we propose to fine-tune state-of-art transformer based models for Arabic such as AraBERTv0.2-base as well as to leverage additional training data from last year's shared task (CheckThat! Lab 2020) along with the dataset provided this year. According to the official evaluation, our submission obtained a joint 4th position in the competition where seven other groups participated

Automated fact-checking: A survey

As online false information continues to grow, automated fact-checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact-checking datasets, devising automated fact-checking pipelines and proposing NLP methods to further research in the development of different components. This article reviews relevant research on automated fact-checking covering both the claim detection and claim validation components