With social media usage growing exponentially in the past few years, fake news has also become extremely prevalent. The detrimental impact of fake news emphasizes the need for research focused on automating the detection of false information and verifying its accuracy. In this work, we present the outcome of the Factify 2 shared task, which provides a multi-modal fact verification and satire news dataset, as part of the DeFactify 2 workshop at AAAI'23. The data calls for a comparison based approach to the task by pairing social media claims with supporting documents, with both text and image, divided into 5 classes based on multi-modal relations. In the second iteration of this task we had over 60 participants and 9 final test-set submissions. The best performances came from the use of DeBERTa for text and Swinv2 and CLIP for image. The highest F1 score averaged for all five classes was 81.82%.

翻译：随着过去几年社交媒体使用量的指数级增长，虚假新闻也变得极为普遍。虚假新闻的破坏性影响凸显了研究自动化检测虚假信息并验证其准确性的必要性。本文介绍了Factify 2共享任务的成果，该任务提供了多模态事实核查与讽刺新闻数据集，作为AAAI'23会议中DeFactify 2研讨会的一部分。该数据要求采用基于比较的方法完成任务，将社交媒体主张与支持性文档（包含文本和图像）配对，并根据多模态关系分为5个类别。在本次任务的第二次迭代中，我们共收到超过60名参与者提交的9份最终测试集结果。最佳表现来自使用DeBERTa处理文本、Swinv2及CLIP处理图像的方法。五个类别的最高平均F1分数达到81.82%。