Analyzing Clusters and Constellations from Untwisting Shortened Links on Twitter Using Conceptual Graphs

The analysis of big data, although potentially a very rewarding task, can present difficulties due to the complexity inherent to such datasets. We suggest that conceptual graphs provide a mechanism for representing knowledge about a domain that can also be used as a useful scaffold for big data analysis. Conceptual graphs may be used as a means to collaboratively build up a robust model forming the skeleton of a data analysis project. This paper describes a case study in which conceptual graphs were used to underpin an exploration of a corpus of tweets relating to the Transportation Security Administration (TSA). Through this process we will demonstrate the emerging model built up of the data landscape involved and of the business structures that underlie the technical frameworks relied upon by microblogging software.

[1]  Bertrand De Longueville,et al.  "OMG, from here, I can see the flames!": a use case of mining location based social networks to acquire spatio-temporal data on forest fires , 2009, LBSN '09.

[2]  Ben Shneiderman,et al.  Systematic yet flexible discovery: guiding domain experts through exploratory data analysis , 2008, IUI '08.

[3]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[4]  Alexander F. Gelbukh,et al.  Detecting Deviations in Text Collections: An Approach Using Conceptual Graphs , 2002, MICAI.

[5]  Jon Kleinberg,et al.  Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter , 2011, WWW.

[6]  Nada Matta,et al.  A Proposition for Managing Project Memory in Concurrent Engineering , 1998 .

[7]  Mark Dredze,et al.  You Are What You Tweet: Analyzing Twitter for Public Health , 2011, ICWSM.

[8]  Carlos A. Coello Coello,et al.  MICAI 2002: Advances in Artificial Intelligence , 2002, Lecture Notes in Computer Science.

[9]  Patrick Paroubek,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2010, LREC.

[10]  Acm Sigsoft.  Scrutinizing Agile Practices or Shoot-Out at the Agile Corral Workshop 2008 (APOS 2008) : held at ICSE 2008 : Leipzig, Germany 10 May 2008 , 2008 .

[11]  Nicola Santoro,et al.  Time-Varying Graphs and Social Network Analysis: Temporal Indicators and Metrics , 2011, ArXiv.

[12]  Anand Kumar,et al.  Text mining and ontologies in biomedicine: Making sense of raw text , 2005, Briefings Bioinform..

[13]  Kristina Lerman,et al.  Information Contagion: An Empirical Study of the Spread of News on Digg and Twitter Social Networks , 2010, ICWSM.

[14]  Barry Smyth,et al.  Using twitter to recommend real-time topical news , 2009, RecSys '09.

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

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

[17]  Fakhri Karray,et al.  Enhancing Text Retrieval Performance using Conceptual Ontological Graph , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[18]  John F. Sowa,et al.  Conceptual Structures: Information Processing in Mind and Machine , 1983 .

[19]  Peter Szolovits,et al.  What Is a Knowledge Representation? , 1993, AI Mag..

[20]  David Gelperin.  Exploring agile , 2008, APOS '08.

[21]  Fang Wu,et al.  Social Networks that Matter: Twitter Under the Microscope , 2008, First Monday.

[22]  Philippe Kruchten,et al.  Proceedings of the 2008 international workshop on Scrutinizing agile practices or shoot-out at the agile corral , 2008, ICSE 2008.

[23]  Roger T. Hartley,et al.  The effect of data structures modifications on algorithms for reasoning operations using a conceptual graphs knowledge base , 2007 .

[24]  Jonathan Sullivan,et al.  A tale of two microblogs in China , 2012 .

[25]  Roger T. Hartley,et al.  Temporal, spatial, and constraint handling in the Conceptual Programming environment, CP , 1992, J. Exp. Theor. Artif. Intell..

[26]  Roger C. Schank,et al.  Scripts, plans, goals and understanding: an inquiry into human knowledge structures , 1978 .

[27]  Frank Maurer,et al.  Extreme Programming: Rapid Development for Web-Based Applications , 2002, IEEE Internet Comput..

[28]  I. Horváth,et al.  TOWARDS PRODUCT-RELATED KNOWLEDGE ASSET WAREHOUSING IN ENTERPRISES , 2002 .

[29]  Krishna P. Gummadi,et al.  On word-of-mouth based discovery of the web , 2011, IMC '11.

[30]  Cecilia Mascolo,et al.  Temporal distance metrics for social network analysis , 2009, WOSN '09.

[31]  Miles Osborne,et al.  Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT '10) , 2010 .

[32]  Shashi Shekhar,et al.  Computational Modeling of Spatio-temporal Social Networks: A Time-Aggregated Graph Approach , 2010 .

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

[34]  Kent L. Beck.  Extreme Programming , 1999, TOOLS.

[35]  Chien-Chung Chan,et al.  Mining pharmaceutical spam from Twitter , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[36]  Patty Kostkova,et al.  Early Warning and Outbreak Detection Using Social Networking Websites: The Potential of Twitter , 2009, eHealth.

[37]  Fernand Gobet,et al.  A theory-driven testing methodology for developing scientific software , 2012, J. Exp. Theor. Artif. Intell..

[38]  Bernard Moulin,et al.  Temporal Contexts for Discourse Representation: An Extension of the Conceptual Graph Approach , 1997, Applied Intelligence.

[39]  Dawn Xiaodong Song,et al.  Suspended accounts in retrospect: an analysis of twitter spam , 2011, IMC '11.

[40]  Anton Yuryev,et al.  Extracting human protein interactions from MEDLINE using a full-sentence parser , 2004, Bioinform..

[41]  Gilad Mishne,et al.  Source Code Retrieval using Conceptual Graphs , 2003 .