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Evaluation of Tools and Extension for Fake News Detection

机译:评估假新闻检测的工具和延伸

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Exposing Fake news is required in today's digital era. In this paper, we discussed several ways to detect the misleading content which the general public can follow. We also provide a detailed discussion of existing tools and extension which are already available for fake news detection. We present several systems designed by researchers to fight against misinformation. Several Fact-checking websites are discussed here to help social media users verify the information present in Social-media. The public should access these tools to determine the fabricated content. This paper will help the general public to know the basic techniques for fake news identification. We ran LSTM and BI-LSTM Classifier on existing Kaggle dataset and achieved 91.51% accuracy using Bi-LSTM classifier.
机译:在今天的数字时代需要暴露假新闻。在本文中,我们讨论了几种方法来检测一般公众可以遵循的误导性内容。我们还提供了对现有工具和延期的详细讨论,这些工具和延期已经可用于假新闻检测。我们展示了由研究人员设计的几个系统来抵抗错误信息。这里讨论了几个事实检查网站以帮助社交媒体用户验证社交媒体中存在的信息。公众应该访问这些工具以确定制造的内容。本文将有助于公众了解假新闻识别的基本技巧。我们在现有的Kaggle DataSet上运行LSTM和Bi-LSTM分类器,并使用Bi-LSTM分类器实现了91.51%的精度。

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