Fake News Detection Using Hybrid Approach

Him Gohil, Vandana Joshi, Snehal Gandhi
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Abstract

Over the last few years, fake news has dramatically increased on social media. Fake news can originate from any number of sources and is shared across different social platforms. This type of information is used to spread for fun or economic gain. Our goal is to stop distributing this type of misleading information on social media or any other platform. In this paper, we have proposed a hybrid model (RoBERTa and BERT) to detect fake news. Our proposed architecture is based on the LIAR multi-label dataset. Our model shows promising results.
使用混合方法检测假新闻
在过去几年里,社交媒体上的假新闻急剧增加。假新闻可能来自任何来源,并在不同的社交平台上分享。这类信息被用于传播娱乐或经济利益。我们的目标是阻止这类误导性信息在社交媒体或任何其他平台上传播。在本文中,我们提出了一种混合模型(RoBERTa 和 BERT)来检测假新闻。我们提出的架构基于 LIAR 多标签数据集。我们的模型显示出良好的效果。
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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