CoAID dataset texts with OCR degradations

This is the text of the CoAID dataset dedicated to fake news detection that has been updated to be used in event detection. Cui, Limeng, et Dongwon Lee. 2020. « CoAID: COVID-19 Healthcare Misinformation Dataset ». <em>ArXiv:2006.00885 [Cs]</em>, novembre. http://arxiv.org/abs/2006.00885. Guillaume Bernard. (2022). CoAID dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630405 Some degradations are applied using the DocCreator [1] tool in order to degrade the text of the tweets and to reproduce some common errors found in OCRised documents [2]. [1]: Journet, Nicholas, Muriel Visani, Boris Mansencal, Kieu Van-Cuong, et Antoine Billy. 2017. « DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images ». <em>Journal of Imaging</em> 3 (4): 62. https://doi.org/10.3390/jimaging3040062. [2]: Linhares Pontes, Elvys, Ahmed Hamdi, Nicolas Sidere, et Antoine Doucet. 2019. « Impact of OCR Quality on Named Entity Linking ». In <em>Digital Libraries at the Crossroads of Digital Information for the Future</em>, 11853:102‑15. Lecture Notes in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-34058-2_11. The results of the OCR degradations are as follow: CoAID CER/WER Without Character degradation Phantom degradation Bleed Blur All CoAID CER 2.105 6.358 2.105 2.122 2.616 7.898 CoAID WER 2.494 20.230 2.496 2.580 3.726 20.230本数据集为专为虚假新闻检测设计的CoAID数据集（CoAID dataset），经更新后可用于事件检测。 Cui, Limeng 与 Dongwon Lee. 2020年. 《CoAID: COVID-19 Healthcare Misinformation Dataset》，发表于*ArXiv:2006.00885 [Cs]*，2020年11月。访问链接：http://arxiv.org/abs/2006.00885。 Guillaume Bernard.（2022）. 含多提取特征（稀疏特征与稠密特征）的CoAID数据集（版本1.0）[数据集]. Zenodo. https://doi.org/10.5281/zenodo.6630405。 为还原经光学字符识别（Optical Character Recognition, OCR）处理的文档中常见的各类典型错误，本研究使用DocCreator工具[1]对推文文本进行多种退化处理。 [1] Journet, Nicholas, Muriel Visani, Boris Mansencal, Kieu Van-Cuong 与 Antoine Billy. 2017年. 《DocCreator: A New Software for Creating Synthetic Ground-Truthed Document Images》，发表于*Journal of Imaging* 3(4): 62。https://doi.org/10.3390/jimaging3040062。 [2] Linhares Pontes, Elvys, Ahmed Hamdi, Nicolas Sidere 与 Antoine Doucet. 2019年. 《Impact of OCR Quality on Named Entity Linking》，收录于《Digital Libraries at the Crossroads of Digital Information for the Future》，属于《计算机科学讲义（Lecture Notes in Computer Science）》丛书第11853卷，Cham：施普林格国际出版公司，第102-115页。https://doi.org/10.1007/978-3-030-34058-2_11。 OCR退化处理的效果如下： | 数据集 | 指标 | 无字符退化 | 伪影退化 | 透印退化 | 模糊退化 | 全部退化 | | ------ | ---- | ---------- | -------- | -------- | -------- | -------- | | CoAID | 字符错误率（Character Error Rate, CER） | 2.105 | 6.358 | 2.105 | 2.122 | 2.616 | 7.898 | | CoAID | 词错误率（Word Error Rate, WER） | 2.494 | 20.230 | 2.496 | 2.580 | 3.726 | 20.230 |