Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Mining User's Real Social Circle in Microblog

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Hailong Qin ; Res. Center for Social Comput. & Inf. Retrieval, Harbin Inst. of Technol., Harbin, China ; Ting Liu ; Yanjun Ma

As a media and communication platform, microblog is more and more popular around the world. Users can follow anyone ranges from well-known individuals to real friends, and read their tweets without their permission. Most users follow a large number of celebrities and public media in microblog, however, these celebrities do not necessarily follow all their fans. Such one-way relationship abounds in the user network and is displayed in the forms of users' followees and followers, which make it difficult to identify users' real friends who are contained in the merged list of followees and followers. The aim of this paper is to propose a general algorithm for mining users' real friends in social media and dividing them into different social circles automatically according to the closeness of their relationships. To verify the effectiveness of the proposed algorithm, we build a microblog application which presents the social circles for users identified by the algorithm and enable users to modify the proposed results according to her/his real social circles. We demonstrate that our algorithm is superior to traditional clustering method in terms of F measure and Mean Average Precision.

Published in:

Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on

Date of Conference:

26-29 Aug. 2012