Exploring Writing Pattern with Pop Culture Ingredients for Social User Modeling
Chiyu Cai, Linjing Li, Daniel Dajun Zeng, Hongyuan Ma · 2019
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.