Evidence-aware Fake News Detection with Graph Neural Networks | |
Xu WZ(许伟志)![]() ![]() ![]() | |
2022-04-22 | |
会议名称 | The ACM Web Conference 2022 |
会议日期 | 2022-4-22 |
会议地点 | Lyon, France |
摘要 | The prevalence and perniciousness of fake news has been a critical issue on the Internet, which stimulates the development of auto- matic fake news detection in turn. In this paper, we focus on the evidence-based fake news detection, where several evidences are utilized to probe the veracity of news (i.e., a claim). Most previ- ous methods first employ sequential models to embed the seman- tic information and then capture the claim-evidence interaction based on different attention mechanisms. Despite their effective- ness, they still suffer from two main weaknesses. Firstly, due to the inherent drawbacks of sequential models, they fail to integrate the relevant information that is scattered far apart in evidences for veracity checking. Secondly, they neglect much redundant informa- tion contained in evidences that may be useless or even harmful. To solve these problems, we propose a unified Graph-based sEmantic sTructure mining framework, namely GET in short. Specifically, different from the existing work that treats claims and evidences as sequences, we model them as graph-structured data and capture the long-distance semantic dependency among dispersed relevant snippets via neighborhood propagation. After obtaining contextual semantic information, our model reduces information redundancy by performing graph structure learning. Finally, the fine-grained se- mantic representations are fed into the downstream claim-evidence interaction module for predictions. Comprehensive experiments have demonstrated the superiority of GET over the state-of-the-arts. |
收录类别 | EI |
七大方向——子方向分类 | 自然语言处理 |
国重实验室规划方向分类 | 语音语言处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52161 |
专题 | 模式识别实验室 |
作者单位 | 中科院自动化所 |
推荐引用方式 GB/T 7714 | Xu WZ,Junfei Wu,Qiang Liu,et al. Evidence-aware Fake News Detection with Graph Neural Networks[C],2022. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
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