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Thesis Advisor徐常胜
Degree Grantor中国科学院研究生院
Place of Conferral北京
Keyword社交媒体行为 人口统计属性 关联性 稳定性
Other Abstract





; With the development of mobile Internet and the popularization of social media network, more and more people use a variety of online social media services, resulting in massive social media content which makes users face the problem of information overload. To this end, how to better analyze and understand the users and to provide them with personalized information services have become the main task and challenge of social media research. User demographic attributes, including age, gender, marital status and occupation, are the basis for understanding and conducting user profiling. The massive user-generated multimedia content in the social media network and rich users' behavior information, implicitly reveal the users' personal information, and show important clues to solve the lack and sparseness of user demographic attributes in the social network. Based on above discussion, this paper focuses on how to infer the users' demographic attributes by their social media behaviors. In view of the two characteristics of demographic attributes—relevance and steadiness, the research work of this thesis is carried out in the following three aspects:
1.     We propose a method to relationally infer the user attributes via hypergraph learning. There exist dependency relations between the different demographic attributes of the user. Based on the user's social media behaviors, it is effective to use users' known demographic attributes and their relevance to help infer the unknown demographic attributes. In the hypergragh, each vertex represents a user in the social media, and the hyperedges are used to capture the similarity relations of the user generated content and the relations between attributes. The user attributes inference is formalized into a regularized label similar propagation problem in the constructed hypergraph, which can effectively infer the users’various attributes.
2.     We propose a coupled projection matrix based cross-OSN approach to infer user demographic attributes, which solves the conflicts between dynamicity of behaviors and the steadiness of demographic attributes. The basic assumption for the proposed approach is that, the same user's cross-OSN behaviors are the reflection of his/her demographic attributes in different scenarios. Based on this, the cross-OSN behaviors are collectively projected onto the same space for demographic attribute inference. Experimental evaluation is conducted on a self-collected Google+ and Twitter dataset, and the results demonstrate the effectiveness of cross-OSN based demographic attribute inference.
3.     We propose a cross-OSN method based on multi-source autoencoder for estimating user demographic attributes. Based on the steadiness of user demographic attributes, the method finds the shared behavior pattern of users in different social media networks, resolves the contradiction between relatively stable demographic attribute and dynamic social media behavior, and solves the problem that user labeled data is difficult to obtain. This method which uses the hierarchical learning model finds the user's sharing pattern with the unlabeled users' behaviors in different social media networks to obtain the stable user features, and infers the labeled users' demographic attributes. This method makes full use of a large number of unlabeled users' data to find the shared behavior patterns of different social media networks, and effectively improves the accuracy of user demographic attribute inference.
Document Type学位论文
Affiliation模式识别国家重点实验室(中国科学院 自动化研究所),北京 100190
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
项连城. 基于社交媒体行为的用户人口统计属性推断[D]. 北京. 中国科学院研究生院,2017.
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