基于知识图谱的多特征融合谣言检测方法

为了解决谣言检测中由于缺乏外部知识而导致模型难以感知内隐信息，进而限制了模型挖掘深层信息的能力这个问题，提出了基于知识图谱的多特征融合谣言检测方法（KGMRD）。首先，对于每个事件，将帖子和评论共同构建为一个文本序列，并利用分类器从中提取其中的情感特征，利用ConceptNet基于文本构造其知识图谱，将知识图谱中的实体表示利用注意力机制与文本的语义特征进行聚合，进而得到增强的语义特征表示；其次，在传播结构方面：对于每个事件，基于帖子的传播转发关系构建传播结构图，使用DropEdge对传播结构图进行剪枝，从而得到更有效的传播结构特征；最后，将得到的特征进行融合处理得到一个新的表示。在Weibo、Twitter15和Twitter16 三个真实数据集上，使用SVM-RBF等七个模型作为基线进行了对比实验。实验结果表明：对比当前效果最好的基线，提出的KGMRD方法在Weibo数据集的Acc指标提升了1.1%；在Twitter15和Twitter16数据集的Acc指标上提升了2.2%，实验证明提出的KGMRD方法是合理的、有效的。

Knowledge graph based multi-feature fusion rumor detection

In order to solve the problem that it is difficult for the model to perceive implicit information due to the lack of external knowledge in rumor detection, which limits the ability of the model to mine deep information, this paper proposed knowledge graph based multi-feature fusion rumor detection(KGMRD) method. Firstly, for each event, it constructed posts and comments together into a text sequence and used a classifier to extract the emotional features. This paper constructed a knowledge graph based on text using ConceptNet and aggregated the entity representation in the knowledge graph with the semantic features of text using the attention mechanism, so as to obtain the enhanced semantic feature representation. Secondly, in terms of communication structure, for each event, this paper built its communication structure diagram based on the propagation and forwarding relationship of the post, and used DropEdge to prune the communication structure diagram, so as to obtain more effective communication structure characteristics. Finally, it fused the obtained features to get a new representation and compared seven models including SVM-RBF on three real datasets of Weibo, Twitter15 and Twitter16. The experimental results show that compared with the current baseline with the best effect, the proposed KGMRD method has the best Acc on the Weibo dataset and improves the Acc by 1.1%. And there is a 2.2% improvement on Twitter15 and Twitter16 dataset in Acc. The experiment proves that the proposed KGMRD method is reasonable and effective.