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A multi-view attention-based deep learning system for online deviant content detection

文献类型：期刊论文

作者Liang, Yunji2; Guo, Bin2; Yu, Zhiwen2; Zheng, Xiaolong1; Wang, Zhu2; Tang, Lei3
刊名WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
出版日期2020-09-30
页码24
关键词Deviant content Deep learning Ensemble learning View attention Social media
ISSN号1386-145X
DOI10.1007/s11280-020-00840-9
通讯作者Liang, Yunji(liangyunji@nwpu.edu.cn)
英文摘要With the exponential growth of user-generated content, policies and guidelines are not always enforced in social media, resulting in the prevalence of deviant content violating policies and guidelines. The adverse effects of deviant content are devastating and far-reaching. However, the detection of deviant content from sparse and imbalanced textual data is challenging, as a large number of stakeholders are involved with different stands and the subtle linguistic cues are highly dependent on complex context. To address this problem, we propose a multi-view attention-based deep learning system, which combines random subspace and binary particle swarm optimization (RS-BPSO) to distill content of interest (candidates) from imbalanced data, and applies the context and view attention mechanisms in convolutional neural network (dubbed as SSCNN) for the extraction of structural and semantic features. We evaluate the proposed approach on a large-scale dataset collected from Facebook, and find that RS-BPSO is able to detect whether the content is associated with marijuana with an accuracy of 87.55%, and SSCNN outperforms baselines with an accuracy of 94.50%.
WOS关键词RANDOM SUBSPACE METHOD ; CLASSIFICATION
资助项目2030 National Key AI Program of China[2018AAA0100500] ; natural science foundation of China[61902320] ; natural science foundation of China[71472175] ; natural science foundation of China[71602184] ; natural science foundation of China[71621002] ; fundamental research funds for the central universities[31020180QD140]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000574077000001
出版者SPRINGER
资助机构2030 National Key AI Program of China ; natural science foundation of China ; fundamental research funds for the central universities
源URL[http://ir.ia.ac.cn/handle/173211/42047]  
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Liang, Yunji
作者单位1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Northwestern Polytech Univ, Xian, Peoples R China
3.Changan Univ, Xian, Peoples R China
推荐引用方式
GB/T 7714		 Liang, Yunji,Guo, Bin,Yu, Zhiwen,et al. A multi-view attention-based deep learning system for online deviant content detection[J]. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,2020:24.
APA Liang, Yunji,Guo, Bin,Yu, Zhiwen,Zheng, Xiaolong,Wang, Zhu,&Tang, Lei.(2020).A multi-view attention-based deep learning system for online deviant content detection.WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,24.
MLA Liang, Yunji,et al."A multi-view attention-based deep learning system for online deviant content detection".WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS (2020):24.

入库方式： OAI收割

来源：自动化研究所

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