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Analysis of User's Weight in Microblog Network Based on User Influence and Active Degree 被引量:3
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作者 Jie Lian Yun Liu +2 位作者 Zhen-Jiang Zhang Jun-Jun Cheng Fei Xiong 《Journal of Electronic Science and Technology》 CAS 2012年第4期368-377,共10页
Based on user's in-degree distribution, traditional ranking algorithms of user's weight usually neglect the considerations of the differences among user's followers and the features of user's tweets. In order to a... Based on user's in-degree distribution, traditional ranking algorithms of user's weight usually neglect the considerations of the differences among user's followers and the features of user's tweets. In order to analyze the factors which impact on user's weight, under the analysis of the data collected from SINA Microblog network, this paper discovers that user influence and active degrees are the dominant factors for this issue. The proposed algorithm evaluates user influence by user's follower number, the influence of user's followers and the reciprocity between users. User's active degree is modeled by user's participation and the quality of user's tweets. The models are tested by different data groups to confirm the parameters for the final calculation. Eventually, this paper compares the computational results with the user's ranking order given by the SINA official application. The performance of this algorithm presents a stronger stability on the fluctuant range of the value of user's weight. 展开更多
关键词 HITS algorithm SINA Microblog user influence user rank.
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Impact of user influence on information multi-step communication in a micro-blog
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作者 吴越 胡勇 +1 位作者 何小海 邓垦 《Chinese Physics B》 SCIE EI CAS CSCD 2014年第6期5-12,共8页
User influence is generally considered as one of the most critical factors that affect information cascading spreading. Based on this common assumption, this paper proposes a theoretical model to examine user influenc... User influence is generally considered as one of the most critical factors that affect information cascading spreading. Based on this common assumption, this paper proposes a theoretical model to examine user influence on the information multi-step communication in a micro-biog. The multi-steps of information communication are divided into first-step and non-first-step, and user influence is classified into five dimensions. Actual data from the Sina micro-blog is collected to construct the model by means of an approach based on structural equations that uses the Partial Least Squares (PLS) technique. Our experimental results indicate that the dimensions of the number of fans and their authority significantly impact the information of first-step conxrnunication. Leader rank has a positive impact on both first-step and non-first-step communication. Moreover, global centrality and weight of friends are positively related to the information non-first-step communication, but authority is found to have much less relation to it. 展开更多
关键词 user influence information multi-step communication micro-blog structural equations
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Microblog User Recommendation Based on Particle Swarm Optimization
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作者 Ling Xing Qiang Ma Ling Jiang 《China Communications》 SCIE CSCD 2017年第5期134-144,共11页
Considering that there exists a strong similarity between behaviors of users and intelligence of swarm of agents,in this paper we propose a novel user recommendation strategy based on particle swarm optimization(PSO)f... Considering that there exists a strong similarity between behaviors of users and intelligence of swarm of agents,in this paper we propose a novel user recommendation strategy based on particle swarm optimization(PSO)for Microblog network. Specifically,a PSO-based algorithm is developed to learn the user influence,where not only the number of followers is incorporated,but also the interactions among users(e.g.,forwarding and commenting on other users' tweets). Three social factors,the influence and the activity of the target user,together with the coherence between users,are fused to improve the performance of proposed recommendation strategy. Experimental results show that,compared to the well-known Page Rank-based algorithm,the proposed strategy performs much better in terms of precision and recall and it can effectively avoid a biased result caused by celebrity effect and zombie fans effect. 展开更多
关键词 particle swarm optimization Microblog social network user recommendation user influence
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An index model for measuring microblog users' influence
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作者 Fuyong YUAN Jing FENG Qianqian FU 《Chinese Journal of Library and Information Science》 2012年第4期67-76,共10页
Purpose: The paper aims to build an index model for measuring microblog users' influence by taking microbloggers of Sina Weibo as a research sample.Design/methodology/approach: Our user influence index model empha... Purpose: The paper aims to build an index model for measuring microblog users' influence by taking microbloggers of Sina Weibo as a research sample.Design/methodology/approach: Our user influence index model emphasizes link analysis and user activities in the microblogging network. We conduct experiments to investigate the performance of our model by using data crawled from Sina Weibo.Findings: User influence is correlated to the attention that a user has received from his/her audience,the user's activities and his/her tweets' influence. Experimental results show that our model can reflect microbloggers' influence in a more reasonable way.Research limitations: More factors need to be considered to identify different influential users at different time periods.Practical implications: The results of the study provide us with insights both into the way to measure microblog users' influence and to rank users based on their influence.Originality/value: By combining link analysis and user activities,this index model can reduce the impact of dummy follower accounts on user influence,reflecting a user's real influence in the microblog system. 展开更多
关键词 user influence Audience's attention user activity Tweets' influence
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UGC-Driven Social Influence Study in Online Micro- Blogging Sites
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作者 LI Hui SHEN Bingqing +1 位作者 CUI Jiangtao MA Jianfeng 《China Communications》 SCIE CSCD 2014年第12期141-151,共11页
In Web 2.0 era,the content on a web page is increasingly generated by end users,rather than limited number of administrators.Hence,large number of User Generated Content(UGC) has driven the explosion of content in the... In Web 2.0 era,the content on a web page is increasingly generated by end users,rather than limited number of administrators.Hence,large number of User Generated Content(UGC) has driven the explosion of content in the web.Thanks to UGC,the pattern of web usage has evolved from download dominated way to a hybrid one with both information download and upload.Large number of UGC has unveiled great capacity of information that is unavailable for researchers before,such as individual preferences,social connections,and etc.In this paper,we propose a novel model which studies the UGC in micro-blogging web sites,the largest and fastest information diffusion media online,and evaluate the social influence for an arbitrary individual.Experimental results show that our model outperforms state-of-the-art techniques in social influence evaluation in both the running time and accuracy. 展开更多
关键词 user generated content microblog social influence communications
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Joint model of user check-in activities for point-of-interest recommendation
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作者 Ren Xingyi Song Meina +1 位作者 E Haihong Song Junde 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2016年第4期25-36,共12页
With the rapid development of location-based networks, point-of-interest(POI) recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel o... With the rapid development of location-based networks, point-of-interest(POI) recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity(GTSCP) to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques. 展开更多
关键词 POI recommendation user check-in activities joint probabilistic generative model geographical influence social influence temporal effect content information popularity information
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