College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Saudi Arabia.
Department of Computer Science, Swansea University, Bay Campus, Swansea, United Kingdom.
PLoS One. 2023 Jul 31;18(7):e0287298. doi: 10.1371/journal.pone.0287298. eCollection 2023.
The proliferation of fake news has severe effects on society and individuals on multiple fronts. With fast-paced online content generation, has come the challenging problem of fake news content. Consequently, automated systems to make a timely judgment of fake news have become the need of the hour. The performance of such systems heavily relies on feature engineering and requires an appropriate feature set to increase performance and robustness. In this context, this study employs two methods for reducing the number of feature dimensions including Chi-square and principal component analysis (PCA). These methods are employed with a hybrid neural network architecture of convolutional neural network (CNN) and long short-term memory (LSTM) model called FakeNET. The use of PCA and Chi-square aims at utilizing appropriate feature vectors for better performance and lower computational complexity. A multi-class dataset is used comprising 'agree', 'disagree', 'discuss', and 'unrelated' classes obtained from the Fake News Challenges (FNC) website. Further contextual features for identifying bogus news are obtained through PCA and Chi-Square, which are given nonlinear characteristics. The purpose of this study is to locate the article's perspective concerning the headline. The proposed approach yields gains of 0.04 in accuracy and 0.20 in the F1 score, respectively. As per the experimental results, PCA achieves a higher accuracy of 0.978 than both Chi-square and state-of-the-art approaches.
假新闻的泛滥对社会和个人在多个方面都产生了严重的影响。随着在线内容的快速生成，出现了假新闻内容的挑战性问题。因此，及时判断假新闻的自动化系统成为当务之急。这些系统的性能在很大程度上依赖于特征工程，并且需要适当的特征集来提高性能和鲁棒性。在这种情况下，本研究采用了两种降维方法，包括卡方检验和主成分分析（PCA）。这些方法与卷积神经网络（CNN）和长短时记忆（LSTM）模型的混合神经网络架构 FakeNET 一起使用。使用 PCA 和卡方检验的目的是利用适当的特征向量来提高性能和降低计算复杂度。使用来自 Fake News Challenges (FNC) 网站的“同意”、“不同意”、“讨论”和“不相关”类别的多类数据集。通过 PCA 和卡方检验获得了用于识别虚假新闻的进一步上下文特征，这些特征具有非线性特征。本研究的目的是确定文章标题的观点。所提出的方法在准确性上分别提高了 0.04，在 F1 得分上提高了 0.20。根据实验结果，PCA 的准确性达到了 0.978，高于卡方和最先进方法的准确性。
