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CLACTA: Comment-Level-Attention and Comment-Type-Awareness for Fake News Detection

机译：克拉巴：评论级别 - 关注和评论 - 型假新闻检测的意识

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摘要

There are many popular communication tools for news sharing in recent years. However, propagation of fake news becomes a serious issue concerning the public and government due to openness and rapidity of online communication. It is widely concerned how to automatically detect fake news as soon as possible. Nevertheless, most existing methods do not well utilize comments which contain rich semantic information or ignore their effect. Inspired by the revealing role of some comments to the original post, we propose the neural network model which consists of comment-level-attention (CLA) and comment-type-awareness (CTA) for fake news detection. In CLA, we devise the attention mechanism which considers semantic relation between the post and the comments. Based on attention weights we take the weighted sum of different comment representations for the sample as corresponding comment feature, which can capture key comment information. As similar to stance, we assume comments can gather into several different types naturally. Therefore, in CTA, we store comment type representations by the memory matrix which is learned in the training process of sample stream. Comment feature for the sample is aware of the memory matrix, and then corresponding comment type feature is obtained. We concatenate the above two auxiliary features and learned post feature to help detect fake news. Our validation experiments using the Weibo dataset and Pheme dataset demonstrate the effectiveness of the proposed model.
机译：近年来有许多流行的通信工具供新闻共享。然而，由于在线沟通的开放和快速，假新闻的传播成为公共和政府的严重问题。它广泛关注如何尽快自动检测假新闻。尽管如此，大多数现有方法都不利用含有丰富语义信息或忽略其效果的评论。灵感来自揭示对原始职位的一些评论的展示作用，我们提出了神经网络模型，由评论级别注意（CLA）和评论型意识（CTA）进行假新闻检测。在CLA中，我们设计了考虑职位与评论之间的语义关系的注意机制。基于注意力重量，我们将样本的不同注释表示的加权和作为对应的评论功能，可以捕获密钥评论信息。与立场一样，我们假设评论可以自然地聚集成几种不同类型。因此，在CTA中，我们通过在样本流的培训过程中存储的存储矩阵来存储评论类型表示。示例的评论功能知道内存矩阵，然后获得相应的注释类型功能。我们连接上述两个辅助功能，并学习了帖子功能，以帮助检测假新闻。我们使用Weibo DataSet和Pheme DataSet的验证实验证明了所提出的模型的有效性。

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