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An ensemble machine learning approach through effective feature extraction to classify fake news

机译：通过有效的特征提取来分类假新闻的集合机器学习方法

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摘要

There are numerous channels available such as social media, blogs, websites, etc., through which people can easily access the news. It is due to the availability of these platforms that the dissemination of fake news has become easier. Anyone using these platforms can create and share fake news content based on personal or professional motives. To address the issue of detecting fake news, numerous studies based on supervised and unsupervised learning methods have been proposed. However, all those studies do suffer from a certain limitation of poor accuracy. The reason for poor accuracy can be attributed due to several reasons such as the poor selection of features, inefficient tuning of parameters, imbalanced datasets, etc. In this article, we have proposed an ensemble classification model for detection of the fake news that has achieved a better accuracy compared to the state-of-the-art. The proposed model extracts important features from the fake news datasets, and the extracted features are then classified using the ensemble model comprising of three popular machine learning models namely, Decision Tree, Random Forest and Extra Tree Classifier. We achieved a training and testing accuracy of 99.8% and 44.15% respectively on the Liar dataset. For the ISOT dataset, we achieved the training and testing accuracy of 100%.
机译：有许多渠道可用，如社交媒体，博客，网站等，人们可以轻松访问新闻。这是由于这些平台的可用性，即虚假新闻的传播变得更容易。任何使用这些平台的人都可以根据个人或专业动机创建和分享假新闻内容。为了解决检测假新闻的问题，提出了许多基于监督和无监督的学习方法的研究。然而，所有这些研究确实遭受了一定限制的准确性差。精度差的原因可能是由于诸如特征的差，参数的差效调整，参数的低效调整等原因来归因于本文中的效率，但是，我们提出了一个用于检测已经实现的假新闻的集合分类模型与最先进的相比，更好的准确性。所提出的模型从假新闻数据集中提取重要特征，然后使用包含三种流行的机器学习模型的集合模型来分类提取的功能即，决策树，随机林和额外的树分类器。我们在骗子数据集中分别达到了99.8％和44.15％的培训和测试准确性。对于ISOT DataSet，我们实现了100％的培训和测试准确性。

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