Safeguarding Abila: Discovering Evolving Activist Networks

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This document summarizes two interfaces presented as part of a VAST challenge solution: a News Analyzer and Email Analyzer. The News Analyzer allows exploration of news articles to identify events using search, entity recognition, and visualization tools. The Email Analyzer visualizes email networks through a co-occurrence matrix, radial graph, and word cloud to examine relationships. Both interfaces were designed specifically for the challenge and utilize techniques like search engines, named entity recognition, spectral co-clustering, and D3 visualization.

Daten & Analysen

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

System Hosted At:
http://embers.cs.vt.edu:60051
2

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

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

News Analyzer
Example: How POK Leadership has changed over Time
15
1998 – 1999

News Analyzer
Example: How POK Leadership has changed over Time
16
1998 – 1999
2000 – 2009

News Analyzer
Example: How POK Leadership has changed over Time
17
1998 – 1999
2000 – 2009
2000 – 2014

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

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

Email Analyzer
Packages Used
•  Co-Occurrence Matrix
– Spectral Co-Clustering Algorithm
– Visualization
•  D3
–  Email Radial, Co-Occurrence matrix and Word Cloud
33

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Epidemiological Modeling of News and Rumors on Twitter

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Abstract : Crime prediction is a topic of significant research across the fields of criminology, data mining, city planning, law enforcement, and political science. Crime patterns exist on a spatial level; these patterns can be grouped geographically by physical location, and analyzed contextually based on the region in which crime occurs. This paper proposes a mechanism to parameterize street-level crime, localize crime hotspots, identify correlations between spatiotemporal crime patterns and social trends, and analyze the resulting data for the purposes of knowledge discovery and anomaly detection. The subject of this study is the county of Merseyside in the United Kingdom, over a span of 21 months beginning in December 2010 (monthly) through August 2012. Several types of crime are analyzed in this dataset, including Burglary and Antisocial Behavior. Through this analysis, several interesting findings are drawn about crime in Merseyside, including: hotspots with steadily increasing crime levels, hotspots with unstable crime levels, synchronous changes in crime trends throughout Merseyside as a whole, individual months in which certain hotspots behaved anomalously, and a strong correlation between crime hotspot locations and borough/postal code locations. We believe that this type of statistical and correlative analysis of crime patterns will help law enforcement agencies predict criminal activity, allocate resources, and promote community awareness to reduce overall crime rates. For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu

Merseyside Crime Analysis

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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. Mini Challenge - I 4

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

15. News Analyzer Example: How POK Leadership has changed over Time 15 1998 – 1999

16. News Analyzer Example: How POK Leadership has changed over Time 16 1998 – 1999 2000 – 2009

17. News Analyzer Example: How POK Leadership has changed over Time 17 1998 – 1999 2000 – 2009 2000 – 2014

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. Email Analyzer 19

20. Email Analyzer 20

21. Email Analyzer 21

22. Email Analyzer 22

23. Email Analyzer 23

24. Email Analyzer 24

25. Email Analyzer 25

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. Email Analyzer 27

28. Email Analyzer 28

29. Email Analyzer 29

30. Email Analyzer - Example 30

31. Email Analyzer - Example 31

32. Email Analyzer - Example 32

33. Email Analyzer Packages Used •  Co-Occurrence Matrix – Spectral Co-Clustering Algorithm – Visualization •  D3 –  Email Radial, Co-Occurrence matrix and Word Cloud 33

34. Thank You 92

Safeguarding Abila: Discovering Evolving Activist Networks

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