假新闻检测:一种图挖掘方法

Hasan Hameed Hasan Ahmed Abdulla, Husain Hameed Abdulla
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引用次数: 0

摘要

互联网和社交媒体的激增极大地扩大了信息的传播范围,但也使虚假信息更容易传播。自动化错误信息和虚假信息的分类是一项艰巨的任务,因为专家在确定文章的准确性时必须考虑多种因素。本研究采用图挖掘技术来识别假新闻故事。所研究的语言特征区分了虚假内容和可信信息。使用来自真实世界的数据集对所提出的方法进行了测试,我们的实验表明,它在该数据集上的性能与现有的假新闻检测算法相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fake News Detection: A Graph Mining Approach
The proliferation of the internet and social media has dramatically expanded the reach of information, but it has also made it easier for false information to be disseminated. Automating the classification of misinformation and disinformation is a difficult task, as experts must consider multiple factors when determining the accuracy of an article. This study employs a graph mining technique to identify fake news stories. The linguistic features examined differentiate fake content from credible information. The proposed method was tested using a dataset from the real world, and our experiments demonstrate that it performs similarly to existing fake news detection algorithms on this dataset.
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