Attending Sentences to detect Satirical Fake News
Attending Sentences to detect Satirical Fake News
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发表时间：
2018-08
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通讯作者：
Sohan De Sarkar;Fan Yang;Arjun Mukherjee
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作者：
Sohan De Sarkar;Fan Yang;Arjun Mukherjee
为了防止虚假信息在互联网上传播，讽刺新闻检测非常重要。现有的捕捉新闻讽刺的方法使用机器学习模型，如支持向量机和层次神经网络以及手工设计的特征，但不探索句子和文档的差异。本文提出了一种鲁棒的、分层的深度神经网络方法用于讽刺检测，该方法能够在句子层面和文档层面捕获讽刺。该架构结合了可插拔的通用神经网络，如CNN、GRU和LSTM。在真实世界新闻讽刺数据集上的实验结果显示了显著的性能提升，证明了我们提出的方法的有效性。通过对学习模型的检查，我们发现了一些关键句子的存在，这些句子控制着新闻中讽刺的存在。
Satirical news detection is important in order to prevent the spread of misinformation over the Internet. Existing approaches to capture news satire use machine learning models such as SVM and hierarchical neural networks along with hand-engineered features, but do not explore sentence and document difference. This paper proposes a robust, hierarchical deep neural network approach for satire detection, which is capable of capturing satire both at the sentence level and at the document level. The architecture incorporates pluggable generic neural networks like CNN, GRU, and LSTM. Experimental results on real world news satire dataset show substantial performance gains demonstrating the effectiveness of our proposed approach. An inspection of the learned models reveals the existence of key sentences that control the presence of satire in news.