Knowledge Commons of Institute of Automation,CAS
Enhancing Rumor Detection in Social Media Using Dynamic Propagation Structures | |
Shuai Wang1,2![]() ![]() ![]() ![]() | |
2019 | |
会议名称 | 2019 IEEE International Conference on Intelligence and Security Informatics (ISI) |
页码 | 41-46 |
会议日期 | July, 2019 |
会议地点 | Shenzhen, China |
出版地 | China |
出版者 | IEEE |
摘要 | Social media, such as Facebook and Twitter, has become one of the most important channels for information dissemination. However, these social media platforms are often misused to spread rumors, which has brought about severe social problems, and consequently, there are urgent needs for automatic rumor detection techniques. Existing work on rumor detection concentrates more on the utilization of textual features, but diffusion structure itself can provide critical propagating information in identifying rumors. Previous works which have considered structural information, only utilize limited propagation structures. Moreover, few related research has considered the dynamic evolution of diffusion structures. To address these issues, in this paper, we propose a Neural Model using Dynamic Propagation Structures (NM-DPS) for rumor detection in social media. Firstly, we propose a partition approach to model the dynamic evolution of propagation structure and then use temporal attention based neural model to learn a representation for the dynamic structure. Finally, we fuse the structure representation and content features into a unified framework for effective rumor detection. Experimental results on two real-world social media datasets demonstrate the salience of dynamic propagation structure information and the effectiveness of our proposed method in capturing the dynamic structure. |
关键词 | rumor detection |
学科领域 | 人工智能 |
学科门类 | 工学 |
DOI | 10.1109/ISI.2019.8823266 |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 自然语言处理 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/26086 |
专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
通讯作者 | Qingchao Kong |
作者单位 | 1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, China 2.Shenzhen Artificial Intelligence and Data Science Institute(Longhua), China 3.The State Information Center, Beijing 100045, China 4.University of Chinese Academy of Sciences, China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Shuai Wang,Qingchao Kong,Yuqi Wang,et al. Enhancing Rumor Detection in Social Media Using Dynamic Propagation Structures[C]. China:IEEE,2019:41-46. |
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paper 44.pdf(436KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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