We explore the use of machine learning techniques to classify a news source for generating unreliable news. Since the advent of the Internet, unreliable news and hoaxes have deceived users. Social media and news outlets are spreading false information to increase the number of viewers or as a part of the psychological competition. In this paper, we present an ensemble classifier using a set of marked true and bogus news articles. Here, the authors develop a classification approach based on text using SVM, Random-Forest, Naïve Bayes, Decision Tree as a base learner in Bagging and AdaBoost. The purpose behind the work is to think of an answer that enable the user to classify and filter some of the false material. Accordingly, we show that the best performing classifiers were AdaBoost-LinearSVM and AdaBoost-Random Forest with 90.70% and 80.17% accuracy, respectively.

中文翻译：

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根据通过集成学习获得的内容对不可靠的新闻进行研究和分析（社交媒体上虚假新闻的流行）

我们探索使用机器学习技术对新闻来源进行分类，以生成不可靠的新闻。自Internet出现以来，不可靠的新闻和恶作剧欺骗了用户。社交媒体和新闻媒体正在传播虚假信息，以增加观众数量，或作为心理竞争的一部分。在本文中，我们使用一组标记为真实和伪造的新闻文章来提供整体分类器。在这里，作者开发了一种基于文本的分类方法，使用SVM，Random-Forest，朴素贝叶斯，决策树作为Bagging和AdaBoost的基础学习者。这项工作的目的是想出一个使用户能够分类和过滤一些虚假资料的答案。因此，我们显示效果最好的分类器是AdaBoost-LinearSVM和AdaBoost-Random Forest，分别为90.70％和80。