Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)

Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)
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DOI：
10.1109/access.2020.3019735
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发表时间：
2020-01-01
期刊：
IEEE ACCESS
影响因子：
3.9
通讯作者：
On, Byung-Won
On, Byung-Won
中科院分区：
计算机科学3区
文献类型：
--
作者：
Umer, Muhammad;Imtiaz, Zainab;On, Byung-Won

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人类或机器产生的假新闻的广泛增加对社会和个人产生了政治和社会方面的负面影响。在社交网络时代，新闻的快速轮播使得及时评估其可靠性变得具有挑战性。因此，自动化假新闻检测工具已成为至关重要的需求。为了解决上述问题，结合了 CNN 和 LSTM 功能的混合神经网络架构与两种不同的降维方法（主成分分析 (PCA) 和卡方）结合使用。这项工作提出在将特征向量传递给分类器之前采用降维技术来降低特征向量的维度。为了发展推理，这项工作从假新闻挑战 (FNC) 网站获取了一个数据集，该数据集有四种立场：同意、不同意、讨论和无关。非线性特征被馈送到 PCA 和卡方，为假新闻检测提供更多上下文特征。这项研究的动机是确定新闻文章与其标题的相对立场。所提出的模型在准确度和 F1 分数方面将结果提高了约 4% 和约 20%。实验结果表明，PCA 优于卡方和最先进的方法，准确率为 97.8%。
Society and individuals are negatively influenced both politically and socially by the widespread increase of fake news either way generated by humans or machines. In the era of social networks, the quick rotation of news makes it challenging to evaluate its reliability promptly. Therefore, automated fake news detection tools have become a crucial requirement. To address the aforementioned issue, a hybrid Neural Network architecture, that combines the capabilities of CNN and LSTM, is used with two different dimensionality reduction approaches, Principle Component Analysis (PCA) and Chi-Square. This work proposed to employ the dimensionality reduction techniques to reduce the dimensionality of the feature vectors before passing them to the classifier. To develop the reasoning, this work acquired a dataset from the Fake News Challenges (FNC) website which has four types of stances: agree, disagree, discuss, and unrelated. The nonlinear features are fed to PCA and chi-square which provides more contextual features for fake news detection. The motivation of this research is to determine the relative stance of a news article towards its headline. The proposed model improves results by similar to 4% and similar to 20% in terms of Accuracy and F1 - score. The experimental results show that PCA outperforms than Chi-square and state-of-the-art methods with 97.8% accuracy.