• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.395-396
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• 2021

우리는 코로나라는 질병으로 인해 우리의 삶이 많이 바뀌었다. 그 중 가장 큰 변화 중 하나는 국가에서 보내는 '재난 알림 문자' 서비스를 실시간으로 보내올 때 마다 확인을 하는 것이다. 그런데 우리는 거주하고 있는 지역 이외에는 다른 지역 재난 문자는 따로 받지를 못한다. 우리가 다른 지역을 방문한 후에 다시 거주하는 지역으로 되돌아오면 방문했던 지역의 코로나 확진자의 동선 문자를 따로 받을 수가 없어서 동선 확인에 어려움을 겪었다. 그리고 방송형 동보 서비스를 이용하면 불필요한 동선의 정보를 받지않고, 우리가 방문했던 곳만 재난 문자를 받을 수 있게 된다. 대중들에게 더 정확하고 확실한 정보를 제공하기 위해서 앱 서비스를 제안하고자 한다.

• 한국안전학회지
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• 제31권6호
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• pp.121-128
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• 2016

Social Media transformed the mass media based information traffic, and it has become a key resource for finding value in enterprises and public institutions. Particularly, in regards to disaster management, the necessity for public participation policy development through the use of social media is emphasized. National Disaster Management Research Institute developed the Social Big Board, which is a system that monitors social Big Data in real time for purposes of implementing social media disaster management. Social Big Board collects a daily average of 36 million tweets in Korean in real time and automatically filters disaster safety related tweets. The filtered tweets are then automatically categorized into 71 disaster safety types. This real time tweet monitoring system provides various information and insights based on the tweets, such as disaster issues, tweet frequency by region, original tweets, etc. The purpose of using this system is to take advantage of the potential benefits of social media in relations to disaster management. It is a first step towards disaster management that communicates with the people that allows us to hear the voice of the people concerning disaster issues and also understand their emotions at the same time. In this paper, Korean language text mining based Social Big Board will be briefly introduced, and disaster issue detection model, which is key algorithms, will be described. Disaster issues are divided into two categories: potential issues, which refers to abnormal signs prior to disaster events, and occurrence issues, which is a notification of disaster events. The detection models of these two categories are defined and the performance of the models are compared and evaluated.