Knowledge Commons of Institute of Automation,CAS
Multi-modal Knowledge-aware Event Memory Network for Social Media Rumor Detection | |
Huaiwen Zhang1,2; Quan Fang1,2; Shengsheng Qian1,2; Changsheng Xu1,2,3 | |
2019-10 | |
会议名称 | ACM international conference on Multimedia |
会议录名称 | MM |
会议日期 | October 21 - 25, 2019 |
会议地点 | Nice, France |
会议录编者/会议主办者 | Association for Computing Machinery |
出版地 | New York, NY, USA |
出版者 | Association for Computing Machinery |
摘要 | The wide dissemination and misleading effects of online rumors on social media have become a critical issue concerning the public and government. Detecting and regulating social media rumors is important for ensuring users receive truthful information and maintaining social harmony. Most of the existing rumor detection methods focus on inferring clues from media content and social context, which largely ignores the rich knowledge information behind the highly condensed text which is useful for rumor verification. Furthermore, existing rumor detection models underperform on unseen events because they tend to capture lots of event-specific features in seen data which cannot be transferred to newly emerged events. In order to address these issues, we propose a novel Multimodal Knowledge-aware Event Memory Network (MKEMN) which utilizes the Multi-modal Knowledge-aware Network (MKN) and Event Memory Network (EMN) as building blocks for social media rumor detection. Specifically, the MKN learns the multi-modal representation of the post on social media and retrieves external knowledge from real-world knowledge graph to complement the semantic representation of short texts of posts and takes conceptual knowledge as additional evidence to improve rumor detection. The EMN extracts event-invariant features of events and stores them into global memory. Given an event representation, the EMN takes it as a query to retrieve the memory network and output the corresponding features shared among events. With the additional information provided by EMN, our model can learn robust representations of events and consistently perform well on the newly emerged events. Extensive experiments on two Twitter benchmark datasets demonstrate that our rumor detection method achieves much better results than state-of-the-art methods. |
关键词 | Social Media Rumor Detection Multi-Modal Knowledge Graph Memory Network |
学科门类 | 工学::计算机科学与技术(可授工学、理学学位) |
DOI | https://doi.org/10.1145/3343031.3350850 |
收录类别 | EI |
七大方向——子方向分类 | 多模态智能 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45011 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
通讯作者 | Changsheng Xu |
作者单位 | 1.National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing 100190, China 2.University of Chinese Academy of Sciences 3.Peng Cheng Laboratory, ShenZhen, China |
推荐引用方式 GB/T 7714 | Huaiwen Zhang,Quan Fang,Shengsheng Qian,et al. Multi-modal Knowledge-aware Event Memory Network for Social Media Rumor Detection[C]//Association for Computing Machinery. New York, NY, USA:Association for Computing Machinery,2019. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
3343031.3350850 (1).(2626KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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