SMART : Social Media Analytics and Reporting Toolkit

We present the Social Media Analytics and Reporting Toolkit (SMART), a web-based visual analytics system that enables the end users to effectively identify actionable information and gain situational awareness from social media channels. The development of SMART has been guided by an iterative design process and close collaborations between visualization researchers and emergency responders. SMART provides real-time social media analysis through topic extraction, cluster examination, anomaly detection and message categorization. These components are integrated into a visual and interactive interface that allows the users to navigate, supervise and customize the exploration of real-time streams. The system has been deployed and used by several law enforcement agencies in several national and regional events for the purpose of real-time monitoring and emergency management.

[1]  David S. Ebert,et al.  Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[2]  David S. Ebert,et al.  Public behavior response analysis in disaster events utilizing visual analytics of microblog data , 2014, Comput. Graph..

[3]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[4]  Yuchen Cui,et al.  Trajectory-based Visual Analytics for Anomalous Human Movement Analysis using Social Media , 2015, EuroVA@EuroVis.

[5]  Irma J. Terpenning,et al.  STL : A Seasonal-Trend Decomposition Procedure Based on Loess , 1990 .

[6]  Axel Platz,et al.  Can twitter really save your life? A case study of visual social media analytics for situation awareness , 2015, 2015 IEEE Pacific Visualization Symposium (PacificVis).

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

[8]  David S. Ebert,et al.  TopoGroups: Context-Preserving Visual Illustration of Multi-Scale Spatial Aggregates , 2017, CHI.

[9]  Thomas Ertl,et al.  ScatterBlogs2: Real-Time Monitoring of Microblog Messages through User-Guided Filtering , 2013, IEEE Transactions on Visualization and Computer Graphics.

[10]  Fernando Diaz,et al.  CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises , 2014, ICWSM.

[11]  Rizal Setya Perdana.  What is Twitter , 2013 .

[12]  Wenyi Huang,et al.  GeoTxt: a web API to leverage place references in text , 2013, GIR '13.

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

[14]  James J. Thomas,et al.  Defining Insight for Visual Analytics , 2009, IEEE Computer Graphics and Applications.

[15]  Thomas Ertl,et al.  Spatiotemporal anomaly detection through visual analysis of geolocated Twitter messages , 2012, 2012 IEEE Pacific Visualization Symposium.

[16]  David S. Ebert,et al.  A Visual Analytics Framework for Microblog Data Analysis at Multiple Scales of Aggregation , 2016, Comput. Graph. Forum.

[17]  Rui Li,et al.  TEDAS: A Twitter-based Event Detection and Analysis System , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[18]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..