On Modeling Community Behaviors and Sentiments in Microblogging

In this paper, we propose the CBS topic model, a probabilistic graphical model, to derive the user communities in microblogging networks based on the sentiments they express on their generated content and behaviors they adopt. As a topic model, CBS can uncover hidden topics and derive user topic distribution. In addition, our model associates topicspecific sentiments and behaviors with each user community. Notably, CBS has a general framework that accommodates multiple types of behaviors simultaneously. Our experiments on two Twitter datasets show that the CBS model can effectively mine the representative behaviors and emotional topics for each community. We also demonstrate that CBS model perform as well as other state-of-the-art models in modeling topics, but outperforms the rest in mining user

[1]  Mohak Shah,et al.  Evaluating Learning Algorithms: A Classification Perspective , 2011 .

[2]  William W. Cohen,et al.  Block-LDA: Jointly Modeling Entity-Annotated Text and Entity-Entity Links , 2014, Handbook of Mixed Membership Models and Their Applications.

[3]  Ana-Maria Popescu,et al.  Democrats, republicans and starbucks afficionados: user classification in twitter , 2011, KDD.

[4]  J. Hendricks.  Communicator-in-chief : how Barack Obama used new media technology to win the White House , 2010 .

[5]  Feida Zhu,et al.  It Is Not Just What We Say, But How We Say Them: LDA-based Behavior-Topic Model , 2013, SDM.

[6]  Ramesh Nallapati,et al.  Joint latent topic models for text and citations , 2008, KDD.

[7]  Hongfei Yan,et al.  Comparing Twitter and Traditional Media Using Topic Models , 2011, ECIR.

[8]  L. Venkata Subramaniam,et al.  Using content and interactions for discovering communities in social networks , 2012, WWW.

[9]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[10]  Ee-Peng Lim,et al.  Politics, sharing and emotion in microblogs , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[11]  David M. Blei,et al.  Connections between the lines: augmenting social networks with text , 2009, KDD.

[12]  Jiawei Han,et al.  Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling , 2012, TIST.

[13]  Jacob Ratkiewicz,et al.  Truthy: mapping the spread of astroturf in microblog streams , 2010, WWW.

[14]  Bernard J. Jansen,et al.  Twitter power: Tweets as electronic word of mouth , 2009, J. Assoc. Inf. Sci. Technol..

[15]  Derek Ruths,et al.  Classifying Political Orientation on Twitter: It's Not Easy! , 2013, ICWSM.

[16]  Partha Pratim Talukdar,et al.  Experiments in Graph-Based Semi-Supervised Learning Methods for Class-Instance Acquisition , 2010, ACL.

[17]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[18]  Isabell M. Welpe,et al.  Divided They Tweet: The Network Structure of Political Microbloggers and Discussion Topics , 2011, ICWSM.

[19]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[20]  Stefan Stieglitz,et al.  Political Communication and Influence through Microblogging--An Empirical Analysis of Sentiment in Twitter Messages and Retweet Behavior , 2012, 2012 45th Hawaii International Conference on System Sciences.

[21]  J. Golbeck,et al.  Twitter use by the U.S. Congress , 2010 .

[22]  Antoine Boutet,et al.  What's in Your Tweets? I Know Who You Supported in the UK 2010 General Election , 2012, ICWSM.