Portraying user life status from microblogging posts

Microblogging services provide a novel and popular communication scheme for Web users to share information and express opinions by publishing short posts, which usually reflect the users' daily life. We can thus model the users' daily status and interests according to their posts. Because of the high complexity and the large amount of the content of the microblog users' posts, it is necessary to provide a quick summary of the users' life status, both for personal users and commercial services. It is non-trivial to summarize the life status of microblog users, particularly when the summary is conducted over a long period. In this paper, we present a compact interactive visualization prototype, LifeCircle, as an efficient summary for exploring the long-term life status of microblog users. The radial visualization provides multiple views for a given microblog user, including annual topics, monthly keywords, monthly sentiments, and temporal trends of posts. We tightly integrate interactive visualization with novel and state-of-the-art microblogging analytics to maximize their advantages. We implement LifeCircle on Sina Weibo, the most popular microblogging service in China, and illustrate the effectiveness of our prototype with various case studies. Results show that our prototype makes users nostalgic and makes them reminiscent about past events, which helps them to better understand themselves and others.

[1]  Scott Counts,et al.  Identifying topical authorities in microblogs , 2011, WSDM '11.

[2]  Ramana Rao,et al.  The Hyperbolic Browser: A Focus + Context Technique for Visualizing Large Hierarchies , 1996, J. Vis. Lang. Comput..

[3]  Keith Andrews,et al.  Information Slices: Visualising and Exploring Large Hierarchies using Cascading, Semi-Circular Discs , 1998 .

[4]  Martin Wattenberg,et al.  Participatory Visualization with Wordle , 2009, IEEE Transactions on Visualization and Computer Graphics.

[5]  Rizal Setya Perdana.  What is Twitter , 2013 .

[6]  Minyi Guo,et al.  Emoticon Smoothed Language Models for Twitter Sentiment Analysis , 2012, AAAI.

[7]  Bongshin Lee,et al.  ManiWordle: Providing Flexible Control over Wordle , 2010, IEEE Transactions on Visualization and Computer Graphics.

[8]  Fernanda B. Viégas,et al.  Visualizing email content: portraying relationships from conversational histories , 2006, CHI.

[9]  Matthew O. Ward,et al.  InterRing: an interactive tool for visually navigating and manipulating hierarchical structures , 2002, IEEE Symposium on Information Visualization, 2002. INFOVIS 2002..

[10]  Jeffrey Heer,et al.  Animated Transitions in Statistical Data Graphics , 2007, IEEE Transactions on Visualization and Computer Graphics.

[11]  M. Sheelagh T. Carpendale,et al.  DocuBurst: Visualizing Document Content using Language Structure , 2009, Comput. Graph. Forum.

[12]  Peter W. Foltz,et al.  An introduction to latent semantic analysis , 1998 .

[13]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[14]  Zhiyuan Liu,et al.  Mining the interests of Chinese microbloggers via keyword extraction , 2012, Frontiers of Computer Science.

[15]  Robert Kosara,et al.  Preconceptions and Individual Differences in Understanding Visual Metaphors , 2009, Comput. Graph. Forum.

[16]  Jonathon Read,et al.  Using Emoticons to Reduce Dependency in Machine Learning Techniques for Sentiment Classification , 2005, ACL.

[17]  Tiejun Zhao,et al.  Target-dependent Twitter Sentiment Classification , 2011, ACL.

[18]  J. Stasko,et al.  Focus+context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations , 2000, IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings.

[19]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[20]  Meredith Ringel Morris,et al.  #TwitterSearch: a comparison of microblog search and web search , 2011, WSDM '11.

[21]  Jimmy J. Lin,et al.  Earlybird: Real-Time Search at Twitter , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[22]  Martin Wattenberg,et al.  TIMELINESTag clouds and the case for vernacular visualization , 2008, INTR.

[23]  Karen Rose,et al.  What is Twitter , 2009 .

[24]  Yarden Livnat,et al.  A visualization paradigm for network intrusion detection , 2005, Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop.

[25]  Balachander Krishnamurthy,et al.  A few chirps about twitter , 2008, WOSN '08.

[26]  Timothy Baldwin,et al.  Lexical Normalisation of Short Text Messages: Makn Sens a #twitter , 2011, ACL.

[27]  Brendan T. O'Connor,et al.  Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments , 2010, ACL.

[28]  Bing Liu,et al.  Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.

[29]  Erik-Jan van der Linden,et al.  Visualizing Business Data with Generalized Treemaps , 2006, IEEE Transactions on Visualization and Computer Graphics.

[30]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

[31]  David B. Dunson,et al.  Probabilistic topic models , 2011, KDD '11 Tutorials.

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

[33]  Osamu Uchida,et al.  A method for automatically generating the emotional vectors of emoticons using weblog articles , 2011 .

[34]  Mark Dredze,et al.  Annotating Named Entities in Twitter Data with Crowdsourcing , 2010, Mturk@HLT-NAACL.

[35]  Shou-De Lin,et al.  Modeling and evaluating information propagation in a microblogging social network , 2012, Social Network Analysis and Mining.

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

[37]  Miles Osborne,et al.  Streaming First Story Detection with application to Twitter , 2010, NAACL.

[38]  Daniel A. Keim,et al.  Monitoring Network Traffic with Radial Traffic Analyzer , 2006, 2006 IEEE Symposium On Visual Analytics Science And Technology.

[39]  Oren Etzioni,et al.  Named Entity Recognition in Tweets: An Experimental Study , 2011, EMNLP.

[40]  Johanna D. Moore,et al.  Twitter Sentiment Analysis: The Good the Bad and the OMG! , 2011, ICWSM.

[41]  Thomas Hofmann,et al.  Probabilistic latent semantic indexing , 1999, SIGIR '99.

[42]  Mary Beth Rosson,et al.  How and why people Twitter: the role that micro-blogging plays in informal communication at work , 2009, GROUP.

[43]  Padraic Neville,et al.  A comparison of 2-D visualizations of hierarchies , 2001, IEEE Symposium on Information Visualization, 2001. INFOVIS 2001..

[44]  Yang Liu,et al.  Interactive Group Suggesting for Twitter , 2011, ACL.

[45]  Dan Klein,et al.  Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network , 2003, NAACL.

[46]  Zhiyuan Liu,et al.  PLDA+: Parallel latent dirichlet allocation with data placement and pipeline processing , 2011, TIST.

[47]  Aron Culotta,et al.  Towards detecting influenza epidemics by analyzing Twitter messages , 2010, SOMA '10.

[48]  Shou-De Lin,et al.  Modeling and Visualizing Information Propagation in a Micro-blogging Platform , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[49]  Richard F. Riesenfeld,et al.  A Survey of Radial Methods for Information Visualization , 2009, IEEE Transactions on Visualization and Computer Graphics.

[50]  Harith Alani,et al.  Alleviating Data Sparsity for Twitter Sentiment Analysis , 2012, #MSM.

[51]  Ming Zhou,et al.  Recognizing Named Entities in Tweets , 2011, ACL.

[52]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.