Enhancing Rumor Detection in Social Media Using Dynamic Propagation Structures | |
Shuai Wang1,2; Qingchao Kong1,3,4![]() ![]() | |
2019 | |
会议日期 | July, 2019 |
会议地点 | Shenzhen, China |
英文摘要 | 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. |
会议录出版者 | IEEE |
会议录出版地 | China |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/26086] ![]() |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
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 |
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]. 见:. Shenzhen, China. July, 2019. |
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