论文标题
使用胶囊神经网络检测假新闻
Detecting Fake News with Capsule Neural Networks
论文作者
论文摘要
近年来，社交媒体中的假新闻大大增加了。这促使需要有效的假新闻检测算法。胶囊神经网络在计算机视觉方面取得了成功，并引起了自然语言处理（NLP）的关注。本文旨在在假新闻检测任务中使用胶囊神经网络。我们为不同长度的新闻项目使用不同的嵌入模型。静态单词嵌入用于简短新闻项目，而在训练阶段允许逐步训练和更新的非静态单词嵌入式用于中长长或大型新闻语句。此外，我们将不同级别的n-gram级用于特征提取。我们提出的架构在该领域的两个著名数据集（即Isot and Liar）上进行了评估。结果表明，令人鼓舞的性能，在ISOT上优于最先进的方法，在验证集上优于3.1％，而骗子数据集的测试集则超过了1％。
Fake news is dramatically increased in social media in recent years. This has prompted the need for effective fake news detection algorithms. Capsule neural networks have been successful in computer vision and are receiving attention for use in Natural Language Processing (NLP). This paper aims to use capsule neural networks in the fake news detection task. We use different embedding models for news items of different lengths. Static word embedding is used for short news items, whereas non-static word embeddings that allow incremental up-training and updating in the training phase are used for medium length or large news statements. Moreover, we apply different levels of n-grams for feature extraction. Our proposed architectures are evaluated on two recent well-known datasets in the field, namely ISOT and LIAR. The results show encouraging performance, outperforming the state-of-the-art methods by 7.8% on ISOT and 3.1% on the validation set, and 1% on the test set of the LIAR dataset.
