DocumentCode :
3678535
Title :
Graph-Based Method for Detecting Occupy Protest Events Using GDELT Dataset
Author :
Fengcai Qiao;Pei Li;Jingsheng Deng;Zhaoyun Ding;Hui Wang
Author_Institution :
Coll. of Inf. Syst. &
fYear :
2015
Firstpage :
164
Lastpage :
168
Abstract :
Recent years have witnessed a series of occupy protest events all over the world. Detecting and monitoring these events is an important and challenging task in social science research and also can provide reference for government´s emergency management. Existing methods mainly solve this problem by document clustering techniques. This paper proposes a novel graph-based occupy protest event detection framework which applies sub graph pattern mining for this task. A wealth of event data about Occupy Wall Street in New York and Occupy Central in Hong Kong from the Global Data on Events, Location, and Tone (GDELT) are utilized in the work. Experimental results on these datasets show that the proposed method can achieve higher detection accuracy with 0.921 on average and MCC value 0.748, outperforming the baseline method.
Keywords :
"Event detection","Data mining","Accuracy","Measurement","Training","Monitoring","Government"
Publisher :
ieee
Conference_Titel :
Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2015 International Conference on
Type :
conf
DOI :
10.1109/CyberC.2015.77
Filename :
7307806
Link To Document :
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