A Closer Look at Fake News Detection: A Deep Learning Perspective
A Closer Look at Fake News Detection: A Deep Learning Perspective
复制标题
仔细观察假新闻检测：深度学习的视角
DOI：
10.1145/3369114.3369149
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Malak Abdullah
中科院分区：
文献类型：
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作者：
Ayat Abedalla;Aisha Al;Malak Abdullah
随着越来越多的人依赖社交媒体获取新闻，假新闻的传播速度越来越快，这被认为是一个问题。由于假新闻对公共决策的负面影响和影响，这引起了研究界的广泛关注。因此，目前的研究努力阐明假新闻问题和使用深度学习方法检测假新闻的过程。使用假新闻挑战（FNC-1）数据集，我们基于文章标题和文章主体之间的关系开发了不同的模型来检测假新闻。我们的模型主要由卷积神经网络（CNN）、长短期记忆网络（LSTM）和双向LSTM （Bi-LSTM）组成。在同一数据集上的其他研究中，他们报告了来自相同训练数据集的测试数据的准确性，与此相反，我们的实验在官方测试数据集上实现了71.2%的准确性。
The increasingly rapid pace of spreading fake news is considered a problem in conjunction with the increasing number of people who are relying upon social media to get news. That earns widespread attention from research communities due to the negative impact and influence of fake news on public decisions. Consequently, the current research strives to illuminate on fake news problem and the process of detecting fake news using deep learning approaches. Using the Fake News Challenge (FNC-1) dataset, we have developed different models to detect fake news based on the relation between article headline and article body. Our models are assembled mainly from Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM) and Bidirectional LSTM (Bi-LSTM). In the contrary of other studies on the same dataset where they reported accuracy for a test data derived from the same training dataset, our experiments achieved 71.2% accuracy for the official testing dataset.
DOI：
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发表时间：
2015-02
期刊：
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影响因子：
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作者：
Ke Xu;Jimmy Ba;Ryan Kiros;Kyunghyun Cho;Aaron C. Courville;R. Salakhutdinov;R. Zemel;Yoshua Bengio-Yoshua-Ben
通讯作者：
Ke Xu;Jimmy Ba;Ryan Kiros;Kyunghyun Cho;Aaron C. Courville;R. Salakhutdinov;R. Zemel;Yoshua Bengio-Yoshua-Ben