TY - GEN
T1 - Exploring Writing Pattern with Pop Culture Ingredients for Social User Modeling
AU - Cai, Chiyu
AU - Li, Linjing
AU - Zeng, Daniel
AU - Ma, Hongyuan
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFC0820105 and 2016QY02D0305, the National Natural Science Foundation of China under Grants 71702181, 71621002, as well as the Key Research Program of the Chinese Academy of Sciences under Grant ZDRW-XH-2017-3. Hongyuan Ma is the corresponding author.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Internet pop culture
KW - attention mechanism
KW - memory network
KW - social user modeling
KW - writing pattern
UR - http://www.scopus.com/inward/record.url?scp=85073186197&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073186197&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2019.8852187
DO - 10.1109/IJCNN.2019.8852187
M3 - Conference contribution
AN - SCOPUS:85073186197
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2019 International Joint Conference on Neural Networks, IJCNN 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Joint Conference on Neural Networks, IJCNN 2019
Y2 - 14 July 2019 through 19 July 2019
ER -