Akhter Muhammad Pervez, Zheng Jiangbin, Afzal Farkhanda, Lin Hui, Riaz Saleem, Mehmood Atif
School of Software and Microelectronics, Northwestern Polytechnical University, Xian, China.
Department of Humanities and Basic Sciences, MCS, National University of Sciences and Technology, Islamabad, Pakistan.
PeerJ Comput Sci. 2021 Mar 9;7:e425. doi: 10.7717/peerj-cs.425. eCollection 2021.
The popularity of the internet, smartphones, and social networks has contributed to the proliferation of misleading information like fake news and fake reviews on news blogs, online newspapers, and e-commerce applications. Fake news has a worldwide impact and potential to change political scenarios, deceive people into increasing product sales, defaming politicians or celebrities, and misguiding visitors to stop visiting a place or country. Therefore, it is vital to find automatic methods to detect fake news online. In several past studies, the focus was the English language, but the resource-poor languages have been completely ignored because of the scarcity of labeled corpus. In this study, we investigate this issue in the Urdu language. Our contribution is threefold. First, we design an annotated corpus of Urdu news articles for the fake news detection tasks. Second, we explore three individual machine learning models to detect fake news. Third, we use five ensemble learning methods to ensemble the base-predictors' predictions to improve the fake news detection system's overall performance. Our experiment results on two Urdu news corpora show the superiority of ensemble models over individual machine learning models. Three performance metrics balanced accuracy, the area under the curve, and mean absolute error used to find that Ensemble Selection and Vote models outperform the other machine learning and ensemble learning models.
互联网、智能手机和社交网络的普及导致了误导性信息的泛滥，如新闻博客、在线报纸和电子商务应用程序上的假新闻和虚假评论。假新闻具有全球影响力，有可能改变政治局势、欺骗人们以增加产品销量、诋毁政治家或名人，以及误导游客不再前往某个地方或国家。因此，找到在线检测假新闻的自动方法至关重要。在过去的几项研究中，重点是英语，但由于标注语料库稀缺，资源匮乏的语言完全被忽视了。在本研究中，我们以乌尔都语来研究这个问题。我们的贡献有三个方面。第一，我们为假新闻检测任务设计了一个乌尔都语新闻文章标注语料库。第二，我们探索了三种单独的机器学习模型来检测假新闻。第三，我们使用五种集成学习方法来整合基本预测器的预测结果，以提高假新闻检测系统的整体性能。我们在两个乌尔都语新闻语料库上的实验结果表明，集成模型优于单独的机器学习模型。使用平衡准确率、曲线下面积和平均绝对误差这三个性能指标发现，集成选择模型和投票模型优于其他机器学习和集成学习模型。
