Abstract With the increasing popularity of social media and web-based forums, the distribution of fake news has become a major threat to various sectors and agencies. This has abated trust in the media, leaving readers in a state of perplexity. There exists an enormous assemblage of research on the theme of Artificial Intelligence (AI) strategies for fake news detection. In the past, much of the focus has been given on classifying online reviews and freely accessible online social networking-based posts. In this work, we propose a deep convolutional neural network (FNDNet) for fake news detection. Instead of relying on hand-crafted features, our model (FNDNet) is designed to automatically learn the discriminatory features for fake news classification through multiple hidden layers built in the deep neural network. We create a deep Convolutional Neural Network (CNN) to extract several features at each layer. We compare the performance of the proposed approach with several baseline models. Benchmarked datasets were used to train and test the model, and the proposed model achieved state-of-the-art results with an accuracy of 98.36% on the test data. Various performance evaluation parameters such as Wilcoxon, false positive, true negative, precision, recall, F1, and accuracy, etc. were used to validate the results. These results demonstrate significant improvements in the area of fake news detection as compared to existing state-of-the-art results and affirm the potential of our approach for classifying fake news on social media. This research will assist researchers in broadening the understanding of the applicability of CNN-based deep models for fake news detection.

中文翻译：

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FNDNet - 用于假新闻检测的深度卷积神经网络

摘要 随着社交媒体和网络论坛的日益普及，虚假新闻的传播已成为各行各业和机构的主要威胁。这削弱了对媒体的信任，让读者处于困惑状态。有大量关于人工智能 (AI) 假新闻检测策略主题的研究。过去，很多重点都放在对在线评论和可免费访问的基于在线社交网络的帖子进行分类上。在这项工作中，我们提出了一种用于假新闻检测的深度卷积神经网络 (FNDNet)。我们的模型 (FNDNet) 不依赖于手工制作的特征，而是旨在通过深度神经网络中内置的多个隐藏层自动学习假新闻分类的判别特征。我们创建了一个深度卷积神经网络 (CNN) 来提取每一层的几个特征。我们将所提出的方法的性能与几个基线模型进行了比较。使用基准数据集来训练和测试模型，所提出的模型在测试数据上以 98.36% 的准确率获得了最先进的结果。使用Wilcoxon、假阳性、真阴性、准确率、召回率、F1、准确率等多种性能评价参数对结果进行验证。与现有的最先进结果相比，这些结果证明了假新闻检测领域的显着改进，并肯定了我们在社交媒体上对假新闻进行分类的方法的潜力。