Beyond Your Interests: Exploring the Information Behind User Tags

Tags have been used in different social medias, such as Delicious, Flickr, LinkedIn and Weibo. In previous work, considerable efforts have been made to make use of tags without identification of their different types. In this study, we argue that tags in user profile indicate three different types of information, say the basics age, status, locality, etc, interests and specialty of a person. Based on this novel user tag taxonomy, we propose a tag classification approach in Weibo to conduct a clearer image of user profiles, which makes use of three categories of features: general statistics feature including user links with followers and followings, content feature and syntax feature. Furthermore, different from many previous studies on tag which concentrate on user specialties, such as expert finding, we find that valuable information can be discovered with the basics and interests user tags. We show some interesting findings in two scenarios, including user profiling with people come from different generations and area profiling with mass appeal, with large scale tag clustering and mining in over 6 million identical tags with 13 million users in Weibo data.

[1]  Cheng Binlin,et al.  Detecting Zombie Followers in Sina Microblog based on the Number of Common Friends , 2013 .

[2]  Yiannis Kompatsiaris,et al.  In & out zooming on time-aware user/tag clusters , 2011, Journal of Intelligent Information Systems.

[3]  Shudong Li,et al.  An Automatic Tag Recommendation Algorithm for Micro-blogging Users , 2013, 2013 International Conference on Computer Sciences and Applications.

[4]  Hector Garcia-Molina,et al.  Social tag prediction , 2008, SIGIR '08.

[5]  Wolfgang Nejdl,et al.  An adaptive teleportation random walk model for learning social tag relevance , 2014, SIGIR.

[6]  Dominik Kowald,et al.  Recommending tags with a model of human categorization , 2013, CIKM.

[7]  Qi Tian,et al.  Exploring tag relevance for image tag re-ranking , 2012, SIGIR '12.

[8]  Krishna P. Gummadi,et al.  Cognos: crowdsourcing search for topic experts in microblogs , 2012, SIGIR '12.

[9]  Marcel Worring,et al.  Unsupervised multi-feature tag relevance learning for social image retrieval , 2010, CIVR '10.

[10]  Roelof van Zwol,et al.  Flickr tag recommendation based on collective knowledge , 2008, WWW.

[11]  Jie Tang,et al.  A Combination Approach to Web User Profiling , 2010, TKDD.

[12]  Ana-Maria Popescu,et al.  A Machine Learning Approach to Twitter User Classification , 2011, ICWSM.

[13]  Zhiyuan Liu,et al.  Expert Finding for Microblog Misinformation Identification , 2012, COLING.

[14]  Rui Li,et al.  Survey on social tagging techniques , 2010, SKDD.

[15]  Yiannis Kompatsiaris,et al.  Co-Clustering Tags and Social Data Sources , 2008, 2008 The Ninth International Conference on Web-Age Information Management.

[16]  Kalina Bontcheva,et al.  GATE: an Architecture for Development of Robust HLT applications , 2002, ACL.

[17]  Kun Yu,et al.  Resume Information Extraction with Cascaded Hybrid Model , 2005, ACL.