TY - GEN
T1 - The event tracking dashboard
T2 - Next-Generation Analyst VI 2018
AU - Giridhar, Prasanna
AU - Lee, Jongdeog
AU - Abdelzaher, Tarek
AU - Kaplan, Lance
N1 - Funding Information:
Research reported in this paper was sponsored in part by the Army Research Laboratory under Cooperative Agreements W911NF-09-2-0053 and W911NF-17-2-0196, in part by DARPA under award W911NF-17-C-0099, and in part by NSF under grants CNS 16-18627 and CNS 13-20209. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory, DARPA, NSF, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
Publisher Copyright:
© 2018 SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2018
Y1 - 2018
N2 - The proliferation of real-time information on social media opens up unprecedented opportunities for situation awareness that arise from extracting unfolding physical events from their social media footprints. The paper describes experiences with a new social media analysis toolkit for detecting and tracking such physical events. A key advantage of the explored analysis algorithms is that they require no prior training, and as such can operate out-of-the-box on new languages, dialects, jargon, and application domains (where by »new», we mean new to the machine), including detection of protests, natural disasters, acts of terror, accidents, and other disruptions. By running the toolkit over a period of time, patterns and anomalies are also detected that offer additional insights and understanding. Through analysis of contemporary political, military, and natural disaster events, the work explores the limits of the training-free approach and demonstrates promise and applicability.
AB - The proliferation of real-time information on social media opens up unprecedented opportunities for situation awareness that arise from extracting unfolding physical events from their social media footprints. The paper describes experiences with a new social media analysis toolkit for detecting and tracking such physical events. A key advantage of the explored analysis algorithms is that they require no prior training, and as such can operate out-of-the-box on new languages, dialects, jargon, and application domains (where by »new», we mean new to the machine), including detection of protests, natural disasters, acts of terror, accidents, and other disruptions. By running the toolkit over a period of time, patterns and anomalies are also detected that offer additional insights and understanding. Through analysis of contemporary political, military, and natural disaster events, the work explores the limits of the training-free approach and demonstrates promise and applicability.
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U2 - 10.1117/12.2306712
DO - 10.1117/12.2306712
M3 - Conference contribution
AN - SCOPUS:85049682157
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Next-Generation Analyst VI
A2 - Llinas, James
A2 - Hanratty, Timothy P.
PB - SPIE
Y2 - 16 April 2018 through 17 April 2018
ER -