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http://dx.doi.org/10.3745/KTSDE.2016.5.7.345

Keyword Filtering about Disaster and the Method of Detecting Area in Detecting Real-Time Event Using Twitter  

Ha, Hyunsoo (가톨릭대학교 컴퓨터공학과)
Hwang, Byung-Yeon (가톨릭대학교 컴퓨터정보공학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.5, no.7, 2016 , pp. 345-350 More about this Journal
Abstract
This research suggests the keyword filtering about disaster and the method of detecting area in real-time event detecting system by analyzing contents of twitter. The diffusion of smart-mobile has lead to a fast spread of SNS and nowadays, various researches based on studying SNS are being processed. Among SNS, the twitter has a characteristic of fast diffusion since it is written in 140 words of short paragraph. Therefore, the tweets that are written by twitter users are able to perform a role of sensor. By using these features the research has been constructed which detects the events that have been occurred. However, people became reluctant to open their information of location because it is reported that private information leakage are increasing. Also, problems associated with accuracy are occurred in process of analyzing the tweet contents that do not follow the spelling rule. Therefore, additional designing keyword filtering and the method of area detection on detecting real-time event process were required in order to develop the accuracy. This research suggests the method of keyword filtering about disaster and two methods of detecting area. One is the method of removing area noise which removes the noise that occurred in the local name words. And the other one is the method of determinating the area which confirms local name words by using landmarks. By applying the method of keyword filtering about disaster and two methods of detecting area, the accuracy has improved. It has improved 49% to 78% by using the method of removing area noise and the other accuracy has improved 49% to 89% by using the method of determinating the area.
Keywords
Twitter; Real-Time Event Detect; Detecting Area; Keyword Filtering;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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