Abstract: Social networks, while enabling information exchange among individuals, also serve as fertile grounds for the dissemination of rumors. The succinct nature of social media posts poses a challenge for most rumor detection methods reliant on content semantic features due to the insufficiency of semantic information. Additionally, numerous rumor detection techniques focusing on propagation features often disregard the unique attributes of commenters, leading to inadequate allocation of weights to different user comments. Thus, a network rumor detection approach is proposed, integrating text semantic enhancement and weighted comment stance. Initially, entities and concepts in posts are elucidated via an external knowledge graph to furnish additional contextual information, thereby augmenting semantic comprehension. Subsequently, leveraging pointwise mutual information, the enhanced text is translated into a weighted graph representation, and a weighted graph attention network is employed to assimilate enhanced semantic features of posts. Stance information for each comment within the post is then extracted using a pre-trained stance detection model, with weight values of stance information being learned based on commenters' characteristics. Furthermore, temporal data of comment stances and corresponding commenter sequences are fed into a cross-modal Transformer to glean the temporal features of comment stances. Ultimately, the enhanced semantic features are adaptively merged with the weighted temporal features of comment stances and fed into a multi-layer perceptron for classification. Experimental results on the PHEME and Weibo datasets demonstrate that this method not only achieves an accuracy improvement of over 1.6% compared to the state-of-the-art baseline method but also outperforms best baseline method by at least 12 hours in early rumor detection.

Key words: rumor detection, semantic enhancement, comment stance, graph neural network, knowledge graph

摘要： 社交网络方便人们信息交流的同时也为谣言的传播提供了新的温床。由于社交媒体帖子通常十分精简，因此大多数基于内容语义特征的谣言检测方法面临着语义信息不足的挑战。同时，目前基于传播特征的谣言检测方法常常忽略了评论用户的个体特征，未能合理分配不同用户评论的权重。因此，提出一种结合文本语义增强和评论立场加权的网络谣言检测方法。首先，通过外部知识图谱获取帖子中的实体和概念的解释，以提供更多上下文信息，从而增强语义理解。接着，借助点互信息将增强后的文本转化为加权图表示，并利用加权图注意力网络学习帖子的增强语义特征。然后，通过预训练的立场检测模型提取帖子中每条评论的立场信息，并根据评论用户的特征来学习立场信息的权重值。此外，将评论立场的时序数据和相应的评论用户序列数据输入跨模态的Transformer，以学习评论立场的时序特征。最终，将增强的语义特征与加权的评论立场时序特征进行自适应融合，并输入多层感知机中进行分类。在PHEME 和 Weibo两个数据集上的实验结果表明，该方法不仅准确率高于最先进的基线方法1.6%以上，而且在早期谣言检测方面，比最好的基线方法提前12个小时。

关键词: 谣言检测, 语义增强, 评论立场, 图神经网络, 知识图谱