Attention-based Convolutional Approach for Misinformation Identification from Massive and Noisy Microblog Posts | |
Yu, Feng1,2![]() ![]() ![]() ![]() ![]() | |
发表期刊 | computers & security
![]() |
2019 | |
期号 | 83页码:106-121 |
摘要 | The fast development of social media fuels massive spreading of misinformation, which harm information security at an increasingly severe degree. It is urgent to achieve misinformation identification and early detection in social media. However, two main difficulties hinder the identification of misinformation. First, an event about a piece of suspicious news usually comprises massive microblog posts (hereinafter referred to as post), and it is hard to directly model the event with massive-volume posts. Second, information in social media is of high noise, i.e., most posts about an event have little contribution to misinformation identification. To resolve the difficulty of massive volume, we propose an Event2vec module to learn distributed representations of events in social media. To overcome the difficulty of high noise, we mine significant posts via content and temporal co-attention, which learn importance weights for content and temporal information of events. In this paper, we propose an Attention-based Convolutional Approach for Misinformation Identification (ACAMI) model. The Event2vec module and the co-attention contribute to learning a good representation of an event. Then the Convolutional Neural Network (CNN) can flexibly extract key features scattered among an input sequence and shape high-level interactions among significant features, which help effectively identify misinformation and achieve practical early detection. Experimental results on two typical datasets validate the effectiveness of the ACAMI model on misinformation identification and early detection tasks. |
关键词 | information security social network misinformation identification early detection convolutional neural network |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39033 |
专题 | 模式识别实验室 |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Yu, Feng,Liu, Qiang,Wu, Shu,et al. Attention-based Convolutional Approach for Misinformation Identification from Massive and Noisy Microblog Posts[J]. computers & security,2019(83):106-121. |
APA | Yu, Feng,Liu, Qiang,Wu, Shu,Wang, Liang,&Tan, Tieniu.(2019).Attention-based Convolutional Approach for Misinformation Identification from Massive and Noisy Microblog Posts.computers & security(83),106-121. |
MLA | Yu, Feng,et al."Attention-based Convolutional Approach for Misinformation Identification from Massive and Noisy Microblog Posts".computers & security .83(2019):106-121. |
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 | ||
22ACAMI.pdf(1601KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论