This work introduces a dataset focused on fake news in US political speeches,
specifically examining racial slurs and biases. By scraping and annotating
40,000 news articles, using advanced NLP tools and human verification, we
provide a nuanced understanding of misinformation in political discourse. The
dataset, designed for machine learning and bias analysis, is a critical
resource for researchers, policymakers, and educators. It facilitates the
development of strategies against misinformation and enhances media literacy,
marking a significant contribution to the study of fake news and political
communication. Our dataset, focusing on the analysis of fake news in the
context of the 2024 elections, is publicly accessible for community to work on
fake news identification. Our dataset, focusing on the analysis of fake news in
the context of the 2024 elections, is publicly accessible.

这项研究介绍了一个关于美国政治演讲中的假新闻的数据集，主要研究种族歧视和偏见。通过爬取和注释 40,000 篇新闻文章，使用先进的自然语言处理工具和人工验证，我们提供了对政治言论中的虚假信息的细致理解。该数据集旨在用于机器学习和偏见分析，为研究人员、政策制定者和教育工作者提供了重要资源。它有助于制定应对虚假信息的策略，提升媒体素养，对假新闻和政治沟通的研究作出了重要贡献。我们的数据集侧重于对 2024 年选举背景下的假新闻进行分析，并向公众提供了访问权限。