Social media platforms like Facebook, Twitter, and Instagram have enabled connection and communication on a large scale. It has revolutionized the rate at which information is shared and enhanced its reach. However, another side of the coin dictates an alarming story. These platforms have led to an increase in the creation and spread of fake news. The fake news has not only influenced people in the wrong direction but also claimed human lives. During these critical times of the Covid19 pandemic, it is easy to mislead people and make them believe in fatal information. Therefore it is important to curb fake news at source and prevent it from spreading to a larger audience. We look at automated techniques for fake news detection from a data mining perspective. We evaluate different supervised text classification algorithms on Contraint@AAAI 2021 Covid-19 Fake news detection dataset. The classification algorithms are based on Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT). We also evaluate the importance of unsupervised learning in the form of language model pre-training and distributed word representations using unlabelled covid tweets corpus. We report the best accuracy of 98.41\% on the Covid-19 Fake news detection dataset.

中文翻译：

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评估用于Covid19虚假新闻检测的深度学习方法

诸如Facebook，Twitter和Instagram之类的社交媒体平台已实现了大规模的连接和通信。它彻底改变了信息共享的速度并扩大了信息的覆盖范围。但是，硬币的另一面指示了一个令人震惊的故事。这些平台导致虚假新闻的创建和传播有所增加。假新闻不仅在错误的方向上影响了人们，而且夺走了人们的生命。在Covid19大流行的关键时期，很容易误导人们并使他们相信致命的信息。因此，从源头上遏制虚假新闻并防止其传播到更大的受众非常重要。我们从数据挖掘的角度研究自动检测假新闻的技术。我们在Contraint @ AAAI 2021 Covid-19 Fake新闻检测数据集上评估了不同的监督文本分类算法。分类算法基于卷积神经网络（CNN），长期短期记忆（LSTM）和来自变压器的双向编码器表示（BERT）。我们还使用未标记的covid推文语料库，以语言模型预训练和分布式单词表示的形式，评估了无监督学习的重要性。我们报告了Covid-19假新闻检测数据集的最佳准确性为98.41 \％。我们还使用未标记的covid推文语料库，以语言模型预训练和分布式单词表示的形式，评估了无监督学习的重要性。我们报告了Covid-19假新闻检测数据集的最佳准确性为98.41 \％。我们还使用未标记的covid推文语料库，以语言模型预训练和分布式单词表示的形式，评估了无监督学习的重要性。我们报告了Covid-19假新闻检测数据集的最佳准确性为98.41 \％。