论文标题
使用多数投票技术的假新闻检测
Fake News Detection Using Majority Voting Technique
论文作者
论文摘要
由于网络和社交网络平台的发展，传播信息变得非常容易。人们比以往任何时候都创建和共享更多的信息，这可能是误导性，错误信息或虚假信息。由于可用信息的非结构化性质，假新闻检测是一项至关重要且具有挑战性的任务。近年来，研究人员提供了重大解决方案来解决虚假新闻发现问题，但由于其性质，仍然存在许多开放问题。在本文中，我们提出了多数投票方法来检测假新闻文章。我们使用了假新闻和真实新闻的不同文本属性。 We have used publicly available fake news dataset, comprising of 20,800 news articles among which 10,387 are real and 10,413 are fake news labeled as binary 0 and 1. For the evaluation of our approach, we have used commonly used machine learning classifiers like, Decision Tree, Logistic Regression, XGBoost, Random Forest, Extra Trees, AdaBoost, SVM, SGD and Naive Bayes.使用上述分类器，我们使用多数投票技术构建了一个多模型的假新闻检测系统，以实现更准确的结果。实验结果表明，我们提出的方法的准确性为96.38％，精度为96％，召回96％，F1量度为96％。评估证实，与单个学习技术相比，大多数投票技术取得了更可接受的结果。
Due to the evolution of the Web and social network platforms it becomes very easy to disseminate the information. Peoples are creating and sharing more information than ever before, which may be misleading, misinformation or fake information. Fake news detection is a crucial and challenging task due to the unstructured nature of the available information. In the recent years, researchers have provided significant solutions to tackle with the problem of fake news detection, but due to its nature there are still many open issues. In this paper, we have proposed majority voting approach to detect fake news articles. We have used different textual properties of fake and real news. We have used publicly available fake news dataset, comprising of 20,800 news articles among which 10,387 are real and 10,413 are fake news labeled as binary 0 and 1. For the evaluation of our approach, we have used commonly used machine learning classifiers like, Decision Tree, Logistic Regression, XGBoost, Random Forest, Extra Trees, AdaBoost, SVM, SGD and Naive Bayes. Using the aforementioned classifiers, we built a multi-model fake news detection system using Majority Voting technique to achieve the more accurate results. The experimental results show that, our proposed approach achieved accuracy of 96.38%, precision of 96%, recall of 96% and F1-measure of 96%. The evaluation confirms that, Majority Voting technique achieved more acceptable results as compare to individual learning technique.
