A Fusion Model of Multi-data Sources for User Profiling in Social Media

摘要：

　　User profiling in social media plays an important role in different applications.Most of the existing approaches for user profiling are based on user-generated messages,which is not sufficient for inferring user attributes.With the continuous accumulation of data in social media,integrating multi-data sources has become the inexorable trend for precise user profiling.In this paper,we take advantage of text messages,user metadata,followee information and network representations.In order to integrate seamlessly multi-data sources,we propose a novel fusion model that effectively captures the complementarity and diversity of different sources.In addition,we address the problem of friendshipbased network from previous studies and introduce celebrity ties which enrich the social network and boost the connectivity of different users.Experimental results show that our method outperforms several state-of-the-art methods on a real-world dataset.

关键词： User profiling Social media Multi-data sources Fusion model

作者: Liming Zhang Sihui Fu Shengyi Jiang Rui Bao Yunfeng Zeng

作者单位: School of Information Science and Technology,Guangdong University of Foreign Studies,Guangzhou,China School of Information Science and Technology,Guangdong University of Foreign Studies,Guangzhou,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 3-15

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）