论文标题
使用单词嵌入来分类错误信息的文本相似性
Text Similarity Using Word Embeddings to Classify Misinformation
论文作者
论文摘要
假新闻是过去几年的日益严重的问题，尤其是在选举期间。确定每天流通的所有用户生成的内容中，确定真实的内容和错误是艰难的。技术可以帮助完成这项工作并优化事实检查过程。在这项工作中，我们解决了找到类似内容的挑战，以便能够建议以前可以验证的事实检查文章，从而避免对相同的信息进行多次验证。这在事实检查的合作方法中尤其重要，其中大型团队的成员不知道其他人已经对其他事实进行了检查。
Fake news is a growing problem in the last years, especially during elections. It's hard work to identify what is true and what is false among all the user generated content that circulates every day. Technology can help with that work and optimize the fact-checking process. In this work, we address the challenge of finding similar content in order to be able to suggest to a fact-checker articles that could have been verified before and thus avoid that the same information is verified more than once. This is especially important in collaborative approaches to fact-checking where members of large teams will not know what content others have already fact-checked.
