论文标题
基于深度学习的假新闻检测的多政策框架
A Multi-Policy Framework for Deep Learning-Based Fake News Detection
论文作者
论文摘要
连通性在现代社会中起着不断增长的作用，世界各地的人们都可以轻松获取快速传播的信息。但是，一个更加相互联系的社会可以有意地传播错误的信息。为了减轻虚假新闻的负面影响，必须改善检测方法。这项工作介绍了多政策语句检查器（MPSC），该框架通过使用深度学习技术来分析陈述本身及其相关新闻文章，从而自动化虚假新闻检测，从而预测它似乎是可信的还是可疑的。使用包含真实新闻和虚假新闻的四个合并的数据集评估了拟议的框架。训练了来自变压器（BERT）模型的长期任期内存（LSTM），封闭式复发单元（GRU）和双向编码器表示，以同时评估词汇和句法特征，并评估了其性能。获得的结果表明，多政策分析可靠地识别可疑陈述，这对于虚假新闻检测可能是有利的。
Connectivity plays an ever-increasing role in modern society, with people all around the world having easy access to rapidly disseminated information. However, a more interconnected society enables the spread of intentionally false information. To mitigate the negative impacts of fake news, it is essential to improve detection methodologies. This work introduces Multi-Policy Statement Checker (MPSC), a framework that automates fake news detection by using deep learning techniques to analyze a statement itself and its related news articles, predicting whether it is seemingly credible or suspicious. The proposed framework was evaluated using four merged datasets containing real and fake news. Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bidirectional Encoder Representations from Transformers (BERT) models were trained to utilize both lexical and syntactic features, and their performance was evaluated. The obtained results demonstrate that a multi-policy analysis reliably identifies suspicious statements, which can be advantageous for fake news detection.
