NELA-GT-2019：一个用于研究新闻文章中错误信息的大型多标签新闻数据集
NELA-GT-2019: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articles
摘要：
在本文中，我们提出了NELA-GT-2018数据集（Nørregaard，Horne和Adaldam 2019）的更新版本，名为NELA-GT-2019。NELA-GT-2019包含2019年1月1日至2019年12月31日期间收集的260个来源的112万篇新闻文章。与NELA-GT-2018一样，这些来源来自广泛的主流新闻来源和替代新闻来源。数据集包括来自7个不同评估站点的源级地面实况标签，涵盖多个准确性维度。NELA-GT-2019数据集可在https://doi.org/10.7910/DVN/O7FWPO上找到
英文摘要：
In this paper, we present an updated version of the NELA-GT-2018 dataset (Nørregaard, Horne, and Adalı 2019), entitled NELA-GT-2019. NELA-GT-2019 contains 1.12M news articles from 260 sources collected between January 1st 2019 and December 31st 2019. Just as with NELA-GT-2018, these sources come from a wide range of mainstream news sources and alternative news sources. Included with the dataset are source-level ground truth labels from 7 different assessment sites covering multiple dimensions of veracity. The NELA-GT-2019 dataset can be found at: https://doi.org/10.7910/DVN/O7FWPO
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。
