Rumor verification on social media aims to identify the truth value of a rumor, which is important to decrease the detrimental public effects. A rumor might arouse heated discussions and replies, conveying different stances of users that could be helpful in identifying the rumor. Thus, several works have been proposed to verify a rumor by modelling its entire stance sequence in the time domain. However, these works ignore that such a stance sequence could be decomposed into controversies with different intensities, which could be used to cluster the stance sequences with the same consensus. Besides, the existing stance extractors fail to consider both the impact of all the previously posted tweets and the reply chain on obtaining the stance of a new reply. To address the above problems, in this paper, we propose a novel stance-based network to aggregate the controversies of the stance sequence for rumor verification, termed Filter-based Stance Network (FSNet). As controversies with different intensities are reflected as the different changes of stances, it is convenient to represent different controversies in the frequency domain, but it is hard in the time domain. Our proposed FSNet decomposes the stance sequence into multiple controversies in the frequency domain and obtains the weighted aggregation of them. In specific, FSNet consists of two modules: the stance extractor and the filter block. To obtain better stance features toward the source, the stance extractor contains two stages. In the first stage, the tweet representation of each reply is obtained by aggregating information from all previously posted tweets in a conversation. Then, the features of stance toward the source, i.e., rumor-aware stance, are extracted with the reply chains in the second stage. In the filter block module, a rumor-aware stance sequence is constructed by sorting all the tweets of a conversation in chronological order. Fourier Transform thereafter is employed to convert the stance sequence into the frequency domain, where different frequency components reflect controversies of different intensities. Finally, a frequency filter is applied to explore the different contributions of controversies. We supervise our FSNet with both stance labels and rumor labels to strengthen the relations between rumor veracity and crowd stances. Extensive experiments on two benchmark datasets demonstrate that our model substantially outperforms all the baselines.

社交媒体上的谣言验证旨在识别谣言的真实价值，这对于减少不良公共影响具有重要意义。谣言可能会引起热烈的讨论和回复，传达用户的不同立场，这有助于识别谣言。因此，已经提出了几项工作来通过在时域中建模整个立场序列来验证谣言。然而，这些工作忽略了这样的立场序列可以分解为具有不同强度的争议，这可以用来对具有相同共识的立场序列进行聚类。此外，现有的立场提取器未能考虑所有先前发布的推文和回复链对获取新回复的立场的影响。为了解决上述问题，在本文中，我们提出了一种新颖的基于立场的网络来聚合立场序列的争议以进行谣言验证，称为基于过滤器的立场网络（FSNet）。由于不同强度的争议都体现为立场的不同变化，因此在频域中表示不同的争议很方便，但在时域中却很难表示。我们提出的 FSNet 将姿态序列分解为频域中的多个争议，并获得它们的加权聚合。具体来说，FSNet 由两个模块组成：姿态提取器和过滤器块。为了获得针对源的更好的姿态特征，姿态提取器包含两个阶段。在第一阶段，每个回复的推文表示是通过聚合对话中所有先前发布的推文的信息来获得的。然后，在第二阶段用回复链提取对源的立场特征，即谣言感知立场。在过滤块模块中，通过按时间顺序对对话的所有推文进行排序来构建谣言感知立场序列。随后利用傅里叶变换将立场序列转换到频域，其中不同的频率分量反映了不同强度的争议。最后，应用频率过滤器来探索争议的不同贡献。我们用立场标签和谣言标签来监督 FSNet，以加强谣言真实性和人群立场之间的关系。对两个基准数据集的大量实验表明，我们的模型大大优于所有基线。