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        A Journalism-Based Approach to Feature Selection for Microblog-Oriented Event Detection

        Zhao Jie;He Wenjuan;Lu Xiaoyang;School of Business,Anhui University;  
        In recent years,microblogs have shown much influence in emergency events detection and public safety.Governments and enterprises have to employ effective ways to detect and monitor emergent events in microblogs;otherwise when emergent events in microblogs happen,they will be in a passive situation and even the society' s stability will be affected.At present,researchers usually employ feature-based classification approaches to detect events in microblogs.However,it is very common to get different results when different features are used in event detection,even in the same microblog dataset.Therefore,it has been a critical issue how to select appropriate features for event detection in microblogs.In this paper,we propose a journalism-based approach for selecting features for microblog event detection.Our proposal uses the "5W" feature of news events that has been revealed in journalism researches.In particular,we first construct an initial feature set representing the "5W" features of microblog event,then we perform a cross-fold and iterative procedure to find out the best feature set.We conduct experiments on a real microblog dataset to compare our proposal with four existing methods of feature selection,in terms of various metrics including precision,recall,and F-measure.The results show that our proposal can reduce the number of the selected feature set.In addition,it obtains high performance w.r.t.recall and F-measure in microblog event detection.
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