论文标题
卷积神经网络和深层神经网络的组合用于假新闻检测
Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection
论文作者
论文摘要
如今，人们更喜欢在社交媒体上关注最新消息，因为它便宜，易于访问且迅速传播。但是，它可以散布有意包含虚假信息的伪造或不可靠的低质量新闻。假新闻的传播可能会对人们和社会产生负面影响。鉴于这种问题的严重性，研究人员尽了最大的努力来识别假新闻可能会显示出可以在发布前检测假新闻的系统的模式和特征。在本文中，我们描述了假新闻挑战阶段＃1（FNC-1）数据集，并概述了使用FNC-1数据集构建假新闻检测系统的竞争尝试。使用FNC-1数据集评估了所提出的模型。竞争性数据集被认为是一个悬而未决的问题，并且在全球范围内是一个挑战。该系统的过程意味着使用不同的自然语言处理技术在标题和身体文本列中处理文本。之后，使用肘部截断方法降低了提取的特征，使用软余弦相似性方法找到每对之间的相似性。新功能已输入CNN和DNN深度学习方法。提出的系统以高度准确性检测所有类别，除了不同意类别。结果，该系统的准确性高达84.6％，将其基于有关该数据集的其他竞争研究将其归类为第二个排名。
Nowadays, People prefer to follow the latest news on social media, as it is cheap, easily accessible, and quickly disseminated. However, it can spread fake or unreliable, low-quality news that intentionally contains false information. The spread of fake news can have a negative effect on people and society. Given the seriousness of such a problem, researchers did their best to identify patterns and characteristics that fake news may exhibit to design a system that can detect fake news before publishing. In this paper, we have described the Fake News Challenge stage #1 (FNC-1) dataset and given an overview of the competitive attempts to build a fake news detection system using the FNC-1 dataset. The proposed model was evaluated with the FNC-1 dataset. A competitive dataset is considered an open problem and a challenge worldwide. This system's procedure implies processing the text in the headline and body text columns with different natural language processing techniques. After that, the extracted features are reduced using the elbow truncated method, finding the similarity between each pair using the soft cosine similarity method. The new feature is entered into CNN and DNN deep learning approaches. The proposed system detects all the categories with high accuracy except the disagree category. As a result, the system achieves up to 84.6 % accuracy, classifying it as the second ranking based on other competitive studies regarding this dataset.
