The option of sharing images, videos and audio files on social media opens up new possibilities for distinguishing between false information and fake news on the Internet. Due to the vast amount of data shared every second on social media, not all data can be verified by a computer or a human expert. Here, a check-worthiness analysis can be used as a first step in the fact-checking pipeline and as a filtering mechanism to improve efficiency. This paper proposes a novel way of detecting the check-worthiness in multi-modal tweets. It takes advantage of two classifiers, each trained on a single modality. For image data, extracting the embedded text with an OCR analysis has shown to perform best. By combining the two classifiers, the proposed solution was able to place first in the CheckThat! 2023 Task 1A with an F1 score of 0.7297 achieved on the private test set.

翻译：在社交媒体上分享图片、视频和音频文件的功能，为识别网络虚假信息和假新闻开辟了新途径。由于社交媒体上每秒共享的海量数据，计算机或人类专家无法验证所有信息。在此背景下，可核查性分析可作为事实核查流程的第一步和提升效率的过滤机制。本文提出了一种检测多模态推文可核查性的新方法，该方法利用了两个分别基于单一模态训练的分类器。对于图像数据，通过OCR分析提取嵌入文本表现出最佳性能。通过组合这两个分类器，所提出的方案在CheckThat！2023任务1A中取得了第一名，在私有测试集上F1分数达到0.7297。