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Safeguarding Abila: Discovering Evolving Activist Networks

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Abstract: This paper introduces a system for visual analysis of news articles and emails. The system was developed in response to VAST MiniChallenge 1 and comprises different interfaces for mining textual data and network data.

For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu

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Safeguarding Abila: Discovering Evolving Activist Networks

  1. 1. Safeguarding Abila through Multiple Data Perspectives VAST 2014 Grand Challenge Award: Effective Analysis and Presentation VAST 2014 Mini Challenge 2 Award: Honorable Mention for Effective Presentation Parang Saraf; Patrick Butler; Naren Ramakrishnan Discovery Analytics Center, Department of Computer Science, Virginia Tech 1 Presented By: Parang Saraf
  2. 2. System Hosted At: http://embers.cs.vt.edu:60051 2
  3. 3. VAST Challenge Solution From DAC •  The DAC solution offers three key advantages: 1.  Provides an efficient front-end interface for user-centered exploration of data 2.  Very little analysis or cleaning of data is performed in the backend, thereby helping an analyst to understand the data better !  Example: Faulty news sources or GPS coordinates are displayed 3.  Offers an intuitive interface to present data in several different ways •  Each Interface was designed from scratch specifically for the VAST Challenge 3
  4. 4. Mini Challenge - I 4
  5. 5. Mini Challenge - I •  Two Interfaces: – News Analyzer •  Helps an Analyst explore news articles and Identify Events – Email Analyzer •  Helps an Analyst visualize and examine Email network 5
  6. 6. News Analyzer 6
  7. 7. News Analyzer 7
  8. 8. News Analyzer 8
  9. 9. News Analyzer 9
  10. 10. News Analyzer 10
  11. 11. News Analyzer 11
  12. 12. News Analyzer 12
  13. 13. News Analyzer 13
  14. 14. News Analyzer 14
  15. 15. News Analyzer Example: How POK Leadership has changed over Time 15 1998 – 1999
  16. 16. News Analyzer Example: How POK Leadership has changed over Time 16 1998 – 1999 2000 – 2009
  17. 17. News Analyzer Example: How POK Leadership has changed over Time 17 1998 – 1999 2000 – 2009 2000 – 2014
  18. 18. News Analyzer Packages Used •  Search Engine –  Whoosh (Python search engine library) •  Provides Logical Query of Articles •  Returns Similar articles •  Named Entity Recognition –  Stanford NER Parser •  Provides Person, Organization and Locations in the input document set •  Visualization –  NVD3 •  Line Chart –  D3 •  Word Clouds 18
  19. 19. Email Analyzer 19
  20. 20. Email Analyzer 20
  21. 21. Email Analyzer 21
  22. 22. Email Analyzer 22
  23. 23. Email Analyzer 23
  24. 24. Email Analyzer 24
  25. 25. Email Analyzer 25
  26. 26. Email Analyzer 26 Spectral Co-Clustering •  Given an n x m matrix of n documents and m words, the algorithm performs co-clustering of documents and words. •  The clustering problem is posed in terms of finding minimum cut vertex partitions in a bipartite graph between document and words •  We provide an m x m matrix where rows and columns denote employees and a cell denotes the number of emails exchanged •  Implemented using the scikit-learn package
  27. 27. Email Analyzer 27
  28. 28. Email Analyzer 28
  29. 29. Email Analyzer 29
  30. 30. Email Analyzer - Example 30
  31. 31. Email Analyzer - Example 31
  32. 32. Email Analyzer - Example 32
  33. 33. Email Analyzer Packages Used •  Co-Occurrence Matrix – Spectral Co-Clustering Algorithm – Visualization •  D3 –  Email Radial, Co-Occurrence matrix and Word Cloud 33
  34. 34. Thank You 92

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