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Evidence data for automated fact-checking (AFC) can be in multiple modalitiessuch as text, tables, images, audio, or video. While there is increasinginterest in using images for AFC, previous works mostly focus on detectingmanipulated or fake images. We propose a novel task, chart-based fact-checking,and introduce ChartBERT as the first model for AFC against chart evidence.ChartBERT leverages textual, structural and visual information of charts todetermine the veracity of textual claims. For evaluation, we create ChartFC, anew dataset of 15, 886 charts. We systematically evaluate 75 differentvision-language (VL) baselines and show that ChartBERT outperforms VL models,achieving 63.8% accuracy. Our results suggest that the task is complex yetfeasible, with many challenges ahead.

Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking