The rapid spread of fake news is a serious problem calling for AI solutions. We employ a deep learning based automated detector through a three level hierarchical attention network (3HAN) for fast, accurate detection of fake news. 3HAN has three levels, one each for words, sentences, and the headline, and constructs a news vector: an effective representation of an input news article, by processing an article in an hierarchical bottom-up manner. The headline is known to be a distinguishing feature of fake news, and furthermore, relatively few words and sentences in an article are more important than the rest. 3HAN gives a differential importance to parts of an article, on account of its three layers of attention. By experiments on a large real-world data set, we observe the effectiveness of 3HAN with an accuracy of 96.77%. Unlike some other deep learning models, 3HAN provides an understandable output through the attention weights given to different parts of an article, which can be visualized through a heatmap to enable further manual fact checking.

翻译：虚假新闻的快速传播是一个亟待人工智能解决方案解决的严重问题。我们提出了一种基于深度学习的自动检测器，通过三级分层注意力网络（3HAN）实现快速、准确的假新闻检测。3HAN包含三个层级，分别对应词、句子和标题，通过自底向上的分层方式处理新闻文章，构建新闻向量：即输入新闻文章的有效表示。已知标题是假新闻的一个显著特征，此外，一篇文章中相对较少的词和句子比其余部分更为重要。3HAN凭借其三层注意力机制，对文章各部分赋予不同的重要性权重。通过在大型真实数据集上的实验，我们观察到3HAN的有效性，准确率达到96.77%。与其他深度学习模型不同，3HAN通过赋予文章不同部分的注意力权重提供可理解的输出，这些权重可通过热力图可视化，从而支持进一步的人工事实核查。