Grandmaster: Interactive Text-Based Analytics of Social Media

People use social media resources like Twitter, Facebook, forums etc. to shareand discuss various activities or topics. By aggregating topic trends acrossmany individuals using these services, we seek to construct a richer profileof a person's activities and interests as well as provide a broader context ofthose activities. This profile may then be used in a variety of ways tounderstand groups as a collection of interests and affinities and anindividual's participation in those groups. Our approach considers that muchof these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping ofindividuals with shared interests based on shared conversation, and not onexplicit social software linking them. In this paper, we discuss aproof-of-concept application called Grandmaster built to pull short sections oftext, a person's comments or Twitter posts, together by analysis andvisualization to allow a gestalt understanding of the full collection of allindividuals: how groups are similar and how they differ, based on theirtext inputs.

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

[2]  HeerJeffrey,et al.  D3 Data-Driven Documents , 2011 .

[3]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[4]  Thomas L. Griffiths,et al.  Probabilistic Topic Models , 2007 .

[5]  Daniele Quercia,et al.  Our Twitter Profiles, Our Selves: Predicting Personality with Twitter , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[6]  Alan Ritter,et al.  Unsupervised Modeling of Twitter Conversations , 2010, NAACL.

[7]  Qi Gao,et al.  Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web , 2011, ESWC.

[8]  Daniel M. Dunlavy,et al.  TopicView: Understanding Document Relationships Using Latent Dirichlet Allocation Models. , 2011 .

[9]  John Hannon,et al.  Recommending twitter users to follow using content and collaborative filtering approaches , 2010, RecSys '10.

[10]  Gregory J. Park,et al.  Predicting Dark Triad Personality Traits from Twitter Usage and a Linguistic Analysis of Tweets , 2012, 2012 11th International Conference on Machine Learning and Applications.

[11]  Jennifer Golbeck,et al.  Predicting Personality from Twitter , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[12]  Philip Chan,et al.  Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.

[13]  Duncan J. Watts,et al.  Everyone's an influencer: quantifying influence on twitter , 2011, WSDM '11.

[14]  Owen Kaser,et al.  Tag-Cloud Drawing: Algorithms for Cloud Visualization , 2007, ArXiv.

[16]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[17]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .