Abstract
In this paper, we present our participation in CLEF 2022 CheckThat! Lab’s Task 1 on detecting check-worthy and verifiable claims and attention-worthy and harmful tweets. We participated in all subtasks of Task 1 for Arabic, Bulgarian, Dutch, English, and Turkish datasets. We investigate the impact of fine-tuning various transformer models and how to increase training data size using machine translation. We also use feed-forward networks with the Manifold Mixup regularization for the respective tasks. We are ranked first in detecting factual claims in Arabic and harmful tweets in Dutch. In addition, we are ranked second in detecting check-worthy claims in Arabic and Bulgarian.
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Notes
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We were not able to use Spanish for other languages due to the insufficient time to meet the deadlines of the lab.
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We note that some of the results were absent at the time of submission. Therefore, in our submission we chose the results based on the incomplete results.
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Eyuboglu, A.B., Altun, B., Arslan, M.B., Sonmezer, E., Kutlu, M. (2023). Fight Against Misinformation on Social Media: Detecting Attention-Worthy and Harmful Tweets and Verifiable and Check-Worthy Claims. In: Arampatzis, A., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2023. Lecture Notes in Computer Science, vol 14163. Springer, Cham. https://doi.org/10.1007/978-3-031-42448-9_14
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