Research On Interest Discovery And Information Organization In Social Networks

Online social network service is an online service, platform, or site that focuses on building social networks or social relations among people to help users sharing interests and/or activities between each other [1]. With the comprehensive functionality of flexibly publishing and sharing information, online social network has enjoyed explosive growth during the recent years and evolved in many different application forms (from online social community to online social media network). In the meantime, the huge success of kinds of online social network applications accumulates massive amount of user profiles and activity data, which further offers a great opportunity for researchers to conduct kinds of data analysis and application designing. These already done or ongoing academic researches include the analysis of online friendship network structure, mining of user activity and other dynamic information in online social network, as well as developing kinds of applications in the platform of online social network.In this work, we focus on the angle of analyzing information organization and diffusion in online social network, and conduct researches on system architecture, friendship organization and maintaining, user activity and interest utilization, and event modeling:1) A novel structure of online social community with the characteristics of dynamic interest group structure is proposed. By conducting a detail analysis on the real and large scale online social community with over 63,000 users,2 million posts, and 18 million views, this paper points out the problem of mismatch between flexible user interest and fixed social group structure. Addressing on this problem, we design a novel structure of online social community with the characteristics of dynamic interest group structure and flexible subscribing mechanism, which can provide dynamic and unique content organization for every user automatically.2) The concept of user session and content recommendation in interest group are proposed to improve the design of content recommendation system. The fast-growing user-generated content poses great challenges on the design of efficient content management and delivery in online social network system. By conducting a comprehensive study of online user activities, including content, social, and time characteristics, this paper try to accurately characterize user interest and user context. And the experiment results obtained in a real online social community further justify that our user interest analysis and context analysis can help to improve the quality and efficiency of content management and delivery.3) A general friend recommendation framework based on common interests and a real friend recommendation system in a biology community are designed. By leveraging interest-based features, we also design a general friend recommendation framework, which can characterize user interest in two dimensions:context (location, time) and content, as well as combining domain knowledge to improve friendship recommending quality. To demonstrate the effectiveness of the proposed framework, the Mutagenesis Information CEnter (MICE) providing biologists publishing, sharing and searching discovered mice mutagenesis information was built. Based on the user accessing requests collected from this data center, we design a friend recommendation system for helping biologists finding other interested researchers.4) User-interested topic information organization solution is proposed. Real-time generated discussion information related to some social events is one of the most important content in online social media network. However, in current online social media network, users are constantly swamped by long streams of unstructured, redundant, and sometimes irrelevant messages, while at the same time lacking a comprehensive and well-organized view of social events. This paper proposes an effective and efficient topic information organization solution (called ETree) with an incremental and hierarchical modeling technique for identifying and constructing event theme structures at different granularities. Detailed evaluation results on event modeling of 20 real social events with 3.5 million tweets demonstrate that ETree can efficiently and incrementally generate high-quality topic structures.