Evaluation of Tools and Extension for Fake News Detection

D. Sharma, Sonal Garg, Priya Shrivastava
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引用次数: 10

Abstract

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.
假新闻检测工具评价及扩展
在当今的数字时代,揭露假新闻是必要的。在本文中,我们讨论了几种检测公众可以遵循的误导性内容的方法。我们还提供了一个详细的讨论现有的工具和扩展,已经可用于假新闻检测。我们介绍了几个由研究人员设计的对抗错误信息的系统。这里讨论了几个事实核查网站,以帮助社交媒体用户验证社交媒体上出现的信息。公众应该使用这些工具来确定捏造的内容。本文将帮助公众了解假新闻识别的基本技术。我们在现有的Kaggle数据集上运行LSTM和BI-LSTM分类器,使用BI-LSTM分类器,准确率达到91.51%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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