TeamX at CheckThat! 2023: Multilingual and Multimodal Approach for Check-Worthiness Detection

Check-worthiness detection is a crucial aspect of the fact-checking pipeline. It aids fact-checkers and journalists by highlighting the claims that necessitate verification. This is especially pertinent in today’s era of social media and varied news channels, where numerous actors voice claims on a range of current affairs topics, including political matters, global warming, and the COVID-19 vaccine. Furthermore, during political events, politicians debate their political agendas and make numerous claims on various subjects. These claims, which often lack factual basis, come in all forms. While some of these claims are important, others are not. Given the time-intensive nature of manual fact-checking, the identification of claims worthy of fact-checking becomes critical. Over the years, there have been research efforts aimed at the automatic detection of such claims. To further this research, in past years, CheckThat! Lab has offered check-worthiness detection tasks on political debates and textual modality social media content. For the first time, CheckThat! Lab offered the task for multimodal content. This study reports our participation in subtask-1A, which consists of Arabic and English. For our experiments, we utilized transformer-based models for both unimodal and multimodal models. The performances of the submitted systems, evaluated using the F1-score on the positive class, were 0.671 and 0.300, respectively. Our systems did not rank on the leaderboard as we made late submissions. However, with additional experiments, we achieved 0.684 and 0.362 for English and Arabic, respectively.