Baohua Zhou


2022

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A Progressive Framework for Role-Aware Rumor Resolution
Lei Chen | Guanying Li | Zhongyu Wei | Yang Yang | Baohua Zhou | Qi Zhang | Xuanjing Huang
Proceedings of the 29th International Conference on Computational Linguistics

Existing works on rumor resolution have shown great potential in recognizing word appearance and user participation. However, they ignore the intrinsic propagation mechanisms of rumors and present poor adaptive ability when unprecedented news emerges. To exploit the fine-grained rumor diffusion patterns and generalize rumor resolution methods, we formulate a predecessor task to identify triggering posts, and then exploit their characteristics to facilitate rumor verification. We design a tree-structured annotation interface and extend PHEME dataset with labels on the message level. Data analysis shows that triggers play a critical role in verifying rumors and present similar lingual patterns across irrelevant events. We propose a graph-based model considering the direction and interaction of information flow to implement role-aware rumor resolution. Experimental results demonstrate the effectiveness of our proposed model and progressive scheme.

2021

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基于多质心异质图学习的社交网络用户建模(User Representation Learning based on Multi-centroid Heterogeneous Graph Neural Networks)
Shangyi Ning (宁上毅) | Guanying Li (李冠颖) | Qin Chen (陈琴) | Zengfeng Huang (黄增峰) | Baohua Zhou (周葆华) | Zhongyu Wei (魏忠钰)
Proceedings of the 20th Chinese National Conference on Computational Linguistics

用户建模已经引起了学术界和工业界的广泛关注。现有的方法大多侧重于将用户间的人际关系融入社区,而用户生成的内容(如帖子)却没有得到很好的研究。在本文中,我们通过实际舆情传播相关的分析表明,在舆情传播过程中对用户属性进行研究的重要作用,并且提出了用户资料数据的筛选方法。同时,我们提出了一种通过异构多质心图池为用户捕获更多不同社区特征的建模。我们首先构造了一个由用户和关键字组成的异质图,并在其上采用了一个异质图神经网络。为了方便用户建模的图表示,提出了一种多质心图池化机制,将多质心的集群特征融入到表示学习中。在三个基准数据集上的大量实验表明了该方法的有效性。

2020

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Modeling Evolution of Message Interaction for Rumor Resolution
Lei Chen | Zhongyu Wei | Jing Li | Baohua Zhou | Qi Zhang | Xuanjing Huang
Proceedings of the 28th International Conference on Computational Linguistics

Previous work for rumor resolution concentrates on exploiting time-series characteristics or modeling topology structure separately. However, how local interactive pattern affects global information assemblage has not been explored. In this paper, we attempt to address the problem by learning evolution of message interaction. We model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity. Moreover, we capture the variation of message interaction using a hierarchical framework to better integrate information flow of a rumor cascade. Experiments on PHEME dataset demonstrate our proposed model achieves higher accuracy than existing methods.