Exploring Writing Pattern with Pop Culture Ingredients for Social User Modeling
Chiyu Cai1,2; Linjing Li1; Daniel Zeng1; Hongyuan Ma3
2019
Conference NameThe 2019 International Joint Conference on Neural Networks (IJCNN)
Conference DateJuly 14-19
Conference PlaceBudapest, Hungary
Abstract

Social networks have significantly altered the behavior patterns of netizens all around the world. Therefore, accurate and expressive model of social users is increasingly demanded as it pose great value in a variety of scenarios, such as e-commerce, cyber security, and entertainment to name a few. In this paper, we propose the Pop Culture Attention Writing Model (PAWM) to explore the writing patterns of social users by explicitly capturing the influence of Internet pop culture ingredients with an attention mechanism. The writing pattern representations are learned by a memory network through storing and updating historical latent patterns. We then develop the Deep Social User Model via jointly modeling basic properties of social users, temporal contents, and the learned writing patterns based on PAWM. This paper is the first trial, to the best of our knowledge, which captures Internet pop culture information and applies deep neural network to model user writing pattern. A series of experiments conducted on social bot detection and social user identification demonstrate and validate the effectiveness of the proposed models.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23643
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Affiliation1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.CNCERT/CC
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chiyu Cai,Linjing Li,Daniel Zeng,et al. Exploring Writing Pattern with Pop Culture Ingredients for Social User Modeling[C],2019.
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