Multi-modal Knowledge-aware Event Memory Network for Social Media Rumor Detection | |
Huaiwen Zhang2,3; Quan Fang2,3; Shengsheng Qian2,3; Changsheng Xu1,2,3 | |
2019-10 | |
会议日期 | October 21 - 25, 2019 |
会议地点 | Nice, France |
关键词 | Social Media Rumor Detection Multi-Modal Knowledge Graph Memory Network |
DOI | https://doi.org/10.1145/3343031.3350850 |
英文摘要 | 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. |
源文献作者 | Association for Computing Machinery |
会议录 | MM |
会议录出版者 | Association for Computing Machinery |
会议录出版地 | New York, NY, USA |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/45011] |
专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
通讯作者 | Changsheng Xu |
作者单位 | 1.Peng Cheng Laboratory, ShenZhen, China 2.University of Chinese Academy of Sciences 3.National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing 100190, 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]. 见:. Nice, France. October 21 - 25, 2019. |
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