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
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
基金supported by the National Natural Sciences Foundation of China under Grant No. 61172072the Beijing Natural Science Foundation under Grant No. 4112045the Fundamental Research Funds for the Central Universities under Grant No. 2011YJS215
文摘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.
基金ACKNOWLEDGEMENT This work was partially supported by the National Natural Science Foundation of China under Grants No. 61202179, No. 61173089 SRF for ROCS, SEM and the Fundamental Research Funds for the Central Universities.
文摘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.
