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.

本文介绍了 Fake News Challenge stage #1 (FNC-1) 数据集并概述了使用该数据集构建伪新闻检测系统的竞争尝试。通过使用不同的自然语言处理技术处理标题和正文文本，提取特征并使用软余弦相似性方法计算相似度。该系统使用深度学习方法进行分类，除了 “disagree” 类别外，其余类别均达到高准确性，最终分类准确率达到 84.6％，在该数据集上排名第二。