Too Many Claims to Fact-Check: Prioritizing Political Claims Based on Check-Worthiness
This addresses the challenge of misinformation overload for fact-checkers, though it is incremental as it builds on existing methods.
The paper tackles the problem of prioritizing political claims for fact-checking by proposing a model that uses BERT with additional features like controversial topics and word embeddings, achieving state-of-the-art performance on CLEF Check That! Lab datasets from 2018 and 2019.
The massive amount of misinformation spreading on the Internet on a daily basis has enormous negative impacts on societies. Therefore, we need automated systems helping fact-checkers in the combat against misinformation. In this paper, we propose a model prioritizing the claims based on their check-worthiness. We use BERT model with additional features including domain-specific controversial topics, word embeddings, and others. In our experiments, we show that our proposed model outperforms all state-of-the-art models in both test collections of CLEF Check That! Lab in 2018 and 2019. We also conduct a qualitative analysis to shed light-detecting check-worthy claims. We suggest requesting rationales behind judgments are needed to understand subjective nature of the task and problematic labels.