摘要:
社交媒体在带给人们便利同时，也为谣言的发布和传播提供了平台.目前，大多数的谣言检测方法都是基于文本内容信息，但在社交媒体场景下，文本内容大多是短文本，这类方法往往会因为数据稀疏性的问题导致性能下降.社交网络上的消息传播可建模为图结构，已有研究考虑消息传播结构特点，通过GCN等模型进行谣言检测.GCN依据结构信息聚合邻居来提升节点表示，但有些邻居聚合是无用的，甚至可能带来噪声，使得通过GCN得到的表示并不可靠.此外，这些研究不能有效的突出源帖信息的重要性.针对这些问题提出了一种融合门控的传播图卷积网络模型GUCNH，在GUCNH模型中，首先利用消息转发关系构建信息转发图，通过2个融合门控的图卷积网络模块来聚合邻居节点信息生成节点的表示，融合门控能够对图卷积之前的特征表示和之后的特征表示进行选择与组合，以得到更加可靠的表示.考虑到在转发图中，任意的帖子之间都可能存在相互影响，而不仅仅是基于邻接关系，因此在2个融合门控的图卷积网络模块之间引入多头自注意力模块来建模任意帖子之间的多角度影响.此外，在转发图中，源帖包含的信息往往是最原始、最丰富的，在生成各节点表示之后，选择性的增强了源节点的信息以增强根源信息的影响力.在3个真实数据集上进行的实验表明，提出的模型优于现有的方法.
Abstract:
Social media not only brings convenience to people, but also provides a platform for spreading rumors. Currently, most rumor detection methods are based on text content information. However, in social media scenarios, text content is mostly short text, which often leads to poor performance due to data sparsity. Message propagation on social networks can be modeled as a graph structure. Previous studies have taken into account the characteristics of message propagation structure, and detected rumors through GCN. GCN aggregates neighbors based on structural information to enhance node representation, but some neighbor aggregation is useless and may even cause noise, which making the representation obtained from GCN unreliable. Meanwhile, these methods can not effectively highlight the importance of the source post information. In this paper, we propose a propagation graph convolution network model GUCNH. In GUCNH model, information forwarding graph is constructed first, and the representation of neighbor nodes is aggregated by two fusion gated convolution network modules. Fusion gating can select and combine the feature representation before and after the graph convolution to get a more reliable representation. Considering that in forwarding graph, any post may interact with each other rather than just with its neighbors, a multi-headed self-attention module is introduced between two integrated gated convolution network modules to model the multi-angle influence between posts. In addition, in forwarding graph, the source posts often contain the richest information than reposts. After generating each node representation, we selectively enhance the source node’s information to enhance the influence of the source posts. Experiments on three real datasets show that our proposed model outperforms the existing methods.
百度学术
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