Paper Info
Title
Cooperative, Dynamic Twitter Parsing And Visualization For Dark Network Analysis
Abstract
Developing a network based on Twitter data for social network analysis (SNA) is a common task in most academic domains. The need for real-time analysis is not as prevalent due to the fact that researchers are interested in the analysis of Twitter information after a major event or for an overall statistical or sociological study of general Twitter users. Dark network analysis is a specific field that focuses on criminal, terroristic, or people of interest networks in which evaluating information quickly and making decisions from this information is crucial. We propose a platform and visualization called Dynamic Twitter Network Analysis (DTNA) that incorporates real-time information from Twitter, its subsequent network topology, geographical placement of geotagged tweets on a Google Map, and storage for long-term analysis. The platform provides a SNA visualization that allows the user to interpret and change the search criteria quickly based on visual aesthetic properties built from key dark network utilities with a user interface that can be dynamic, up-to-date for time critical decisions and geographic specific.
Year
DOI
Venue
2013
10.1109/NSW.2013.6609217
PROCEEDINGS OF THE 2013 IEEE 2ND INTERNATIONAL NETWORK SCIENCE WORKSHOP (NSW)
Keywords
Field
DocType
Dark Networks, Visualization, Social Network Analysis, User-Design
Rule-based machine translation,Organizational network analysis,World Wide Web,Data visualization,Computer science,Visualization,Social network analysis,Network topology,Network analysis,User interface
Conference
Citations 
PageRank 
References 
6
0.62
10
Authors
1
Name
Order
Citations
PageRank
Patrick M. Dudas1222.42