Detecting Emergent Conflicts through Web Mining and Visualization

An ocean of data is available on the web. From this ocean of data, information can in theory be extracted and used by analysts for detecting emergent trends (trend spotting). However, to do this manually is a daunting and nearly impossible task. We describe a semi-automatic system in which data is automatically collected from selected sources, and to which linguistic analysis is applied to extract e.g., entities and events. After combining the extracted information with human intelligence reports, the results are visualized to the user of the system who can interact with it in order to obtain a better awareness of historic as well as emergent trends. A prototype of the proposed system has been implemented and some initial results are presented in the paper.

[1]  Michael L. Nelson,et al.  Efficient, automatic web resource harvesting , 2006, WIDM '06.

[2]  Peter Wallensteen,et al.  Armed Conflict 1946-2001: A New Dataset , 2002 .

[3]  H.L. Larsen,et al.  Notice of Violation of IEEE Publication PrinciplesHarvesting Terrorists Information from Web , 2007, 2007 11th International Conference Information Visualization (IV '07).

[4]  Gheorghe Tecuci,et al.  Personal Cognitive Assistants for Military Intelligence Analysis: Mixed-Initiative Learning, Tutoring, and Problem Solving , 2005 .

[5]  J. Iria T-Rex : A Flexible Relation Extraction Framework , 2004 .

[6]  Panagiotis Takis Metaxas,et al.  Limits of Electoral Predictions Using Twitter , 2011, ICWSM.

[7]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[8]  Richard Colbaugh,et al.  Early warning analysis for social diffusion events , 2010, 2010 IEEE International Conference on Intelligence and Security Informatics.

[9]  Eni Mustafaraj,et al.  On the predictability of the U.S. elections through search volume activity , 2011 .

[10]  Marti A. Hearst Trends & Controversies: Mixed-initiative interaction , 1999, IEEE Intell. Syst..

[11]  Sean P. O'Brien,et al.  Crisis Early Warning and Decision Support: Contemporary Approaches and Thoughts on Future Research , 2010 .

[12]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[13]  Matthew A. Russell,et al.  Mining the social web , 2011 .

[14]  K. Weick,et al.  Organizing and the Process of Sensemaking , 2005 .

[15]  Hendrik Blockeel,et al.  Web mining research: a survey , 2000, SKDD.

[16]  Philip A. Schrodt Twenty Years of the Kansas Event Data System Project , 2006 .

[17]  Michael C. Herron Twenty Years of the Kansas Event Data System Project , 2006 .

[18]  Hsinchun Chen,et al.  Terrorism Informatics: Knowledge Management and Data Mining for Homeland Security , 2008 .

[19]  Philip A. Schrodt,et al.  An Event Data Analysis of Third-Party Mediation in the Middle East and Balkans , 2004 .

[20]  Oren Etzioni,et al.  The World-Wide Web: quagmire or gold mine? , 1996, CACM.

[21]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[22]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[23]  H. Varian,et al.  Predicting the Present with Google Trends , 2009 .

[24]  Curry Guinn,et al.  Mixed-initiative interaction , 1999 .

[25]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.