Mapping users across social media platforms by integrating text and structure information
Song Sun1,2; Qiudan Li1; Peng Yan1; Daniel Dajun Zeng1,2,3
2017
会议名称2017 IEEE International Conference on Intelligence and Security Informatics, ISI 2017
页码113-118
会议日期July 22-24, 2017
会议地点Beijing, China
摘要
With the development of social media technology, users often register accounts, post messages and create friend links on several different platforms. Performing user identity mapping on multi-platform based on the behavior patterns of users is considerable for network supervision and personalization service. The existing methods focus on utilizing either text information or structure information alone. However, text information and structure information reflect different aspects of a user. An organic combination of them is beneficial to mining user behavior patterns, thus help identify users across platforms accurately. The challenging problems are the effective representation and similarity computation of the text and structure information. We propose a mapping method which integrates text and structure information. At first, the model represents user name, description, location information based on word2vec or string matching, and friend information represented as relation network is regarded as structure information. Then these information are used for similarity computation using Jaccard index or cosine similarity. After similarity computation, a linear model is adopted to get the overall similarity of user pairs to perform user mapping. Based on the proposed method, we develop a prototype system, which allows users to set and adjust the weights of different information, or set expected index. The experimental results on a real-world dataset demonstrate the efficiency of the proposed model.
关键词User-mapping Cross-platform Similarity Computation Word2vec
收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/15397
专题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
作者单位1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190 China
2.University of Chinese Academy of Sciences, Beijing, China
3.Department of Management Information Systems, University of Arizona, Tucson, AZ 85721, USA
推荐引用方式
GB/T 7714
Song Sun,Qiudan Li,Peng Yan,et al. Mapping users across social media platforms by integrating text and structure information[C],2017:113-118.
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