Fraunhofer SIT 在 CheckThat! 2023：混合单模态分类器以估计多模态推文的核查价值
Fraunhofer SIT at CheckThat! 2023: Mixing Single-Modal Classifiers to Estimate the Check-Worthiness of Multi-Modal Tweets
AI总结：
本文提出一种混合单模态分类器的方法检测多模态推文的核查价值，通过结合文本分类器和基于OCR提取图像文本的分类器，在CheckThat! 2023 Task 1A中以0.7297的F1分数获得第一名。
摘要：
在社交媒体上分享图像、视频和音频文件的选项为区分互联网上的错误信息和假新闻开辟了新的可能性。由于社交媒体上每秒共享的数据量巨大，并非所有数据都能由计算机或人类专家进行核查。在这里，核查价值分析可以用作事实核查管道的第一步，并作为提高效率的过滤机制。本文提出了一种检测多模态推文核查价值的新方法。它利用了两个分类器，每个分类器都在单一模态上进行训练。对于图像数据，使用 OCR 分析提取嵌入文本表现最佳。通过结合这两个分类器，所提出的解决方案在 CheckThat! 2023 Task 1A 中获得第一名，在私有测试集上取得的 F1 分数为 0.7297。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
