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Real-time analysis application for identifying bursty local areas related to emergency topics.

Sakai T, Tamura K - Springerplus (2015)

Bottom Line: Many researchers have been tackling the development of new data mining techniques for georeferenced documents to identify and analyze emergency topics, such as natural disasters, weather, diseases, and other incidents.In particular, the utilization of geotagged tweets to identify and analyze natural disasters has received much attention from administrative agencies recently because some case studies have achieved compelling results.Moreover, we have implemented two types of application interface: a Web application interface and an android application interface.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-Higashi, Asa-Minami-Ku, 731-3194 Hiroshima Japan.

ABSTRACT
Since social media started getting more attention from users on the Internet, social media has been one of the most important information source in the world. Especially, with the increasing popularity of social media, data posted on social media sites are rapidly becoming collective intelligence, which is a term used to refer to new media that is displacing traditional media. In this paper, we focus on geotagged tweets on the Twitter site. These geotagged tweets are referred to as georeferenced documents because they include not only a short text message, but also the documents' posting time and location. Many researchers have been tackling the development of new data mining techniques for georeferenced documents to identify and analyze emergency topics, such as natural disasters, weather, diseases, and other incidents. In particular, the utilization of geotagged tweets to identify and analyze natural disasters has received much attention from administrative agencies recently because some case studies have achieved compelling results. In this paper, we propose a novel real-time analysis application for identifying bursty local areas related to emergency topics. The aim of our new application is to provide new platforms that can identify and analyze the localities of emergency topics. The proposed application is composed of three core computational intelligence techniques: the Naive Bayes classifier technique, the spatiotemporal clustering technique, and the burst detection technique. Moreover, we have implemented two types of application interface: a Web application interface and an android application interface. To evaluate the proposed application, we have implemented a real-time weather observation system embedded the proposed application. we used actual crawling geotagged tweets posted on the Twitter site. The weather observation system successfully detected bursty local areas related to observed emergency weather topics.

No MeSH data available.


Screen shots of Web application interface.(a) shows screen shots of the “snow” observation application that we have implemented, (b) shows screen shots of the “rain” observation application that we have implemented. The Web application interface consists of four components: a map, a ranking table, a chart of bursts, and tag cloud. Icons, which indicate extracted bursty areas, are mapped on the map. If users click an icon, markers, which represents geotagged tweets located in the extracted bursty area are appeared. If the users click each marker, a window including the text data of geotagged tweet is opened. The ranking table is a ranking list of extracted bursty areas. Extracted bursty area are ranked by increasing rate of the number of geotagged tweets. An additional movie file shows this in more detail [see Additional file 1].
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Fig6: Screen shots of Web application interface.(a) shows screen shots of the “snow” observation application that we have implemented, (b) shows screen shots of the “rain” observation application that we have implemented. The Web application interface consists of four components: a map, a ranking table, a chart of bursts, and tag cloud. Icons, which indicate extracted bursty areas, are mapped on the map. If users click an icon, markers, which represents geotagged tweets located in the extracted bursty area are appeared. If the users click each marker, a window including the text data of geotagged tweet is opened. The ranking table is a ranking list of extracted bursty areas. Extracted bursty area are ranked by increasing rate of the number of geotagged tweets. An additional movie file shows this in more detail [see Additional file 1].

Mentions: Figures 6 and 7 show screen shots of the Web application interface and the Android application interface to the real-time weather observation system, which are implemented by us. Figure 6 (a) shows screen shots on February 8, 2014. It snowed heavily in Japan; especially, the Tokyo metropolitan region and Koshin region had heavy snow on February 8, 2014. The icons of snow crystal indicates extracted bursty local areas. Through the system, we can know what weather is going on in Japan. Figure 6 (b) shows screen shots on July 3, 2014. It was rainy in western Japan. The icons of umbrella indicates extracted bursty local areas. If we click or touch these icons, we can observed the details of the selected bursty local areas. An additional movie file shows this in more detail [see Additional file 1].Figure 6


Real-time analysis application for identifying bursty local areas related to emergency topics.

Sakai T, Tamura K - Springerplus (2015)

Screen shots of Web application interface.(a) shows screen shots of the “snow” observation application that we have implemented, (b) shows screen shots of the “rain” observation application that we have implemented. The Web application interface consists of four components: a map, a ranking table, a chart of bursts, and tag cloud. Icons, which indicate extracted bursty areas, are mapped on the map. If users click an icon, markers, which represents geotagged tweets located in the extracted bursty area are appeared. If the users click each marker, a window including the text data of geotagged tweet is opened. The ranking table is a ranking list of extracted bursty areas. Extracted bursty area are ranked by increasing rate of the number of geotagged tweets. An additional movie file shows this in more detail [see Additional file 1].
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4402682&req=5

Fig6: Screen shots of Web application interface.(a) shows screen shots of the “snow” observation application that we have implemented, (b) shows screen shots of the “rain” observation application that we have implemented. The Web application interface consists of four components: a map, a ranking table, a chart of bursts, and tag cloud. Icons, which indicate extracted bursty areas, are mapped on the map. If users click an icon, markers, which represents geotagged tweets located in the extracted bursty area are appeared. If the users click each marker, a window including the text data of geotagged tweet is opened. The ranking table is a ranking list of extracted bursty areas. Extracted bursty area are ranked by increasing rate of the number of geotagged tweets. An additional movie file shows this in more detail [see Additional file 1].
Mentions: Figures 6 and 7 show screen shots of the Web application interface and the Android application interface to the real-time weather observation system, which are implemented by us. Figure 6 (a) shows screen shots on February 8, 2014. It snowed heavily in Japan; especially, the Tokyo metropolitan region and Koshin region had heavy snow on February 8, 2014. The icons of snow crystal indicates extracted bursty local areas. Through the system, we can know what weather is going on in Japan. Figure 6 (b) shows screen shots on July 3, 2014. It was rainy in western Japan. The icons of umbrella indicates extracted bursty local areas. If we click or touch these icons, we can observed the details of the selected bursty local areas. An additional movie file shows this in more detail [see Additional file 1].Figure 6

Bottom Line: Many researchers have been tackling the development of new data mining techniques for georeferenced documents to identify and analyze emergency topics, such as natural disasters, weather, diseases, and other incidents.In particular, the utilization of geotagged tweets to identify and analyze natural disasters has received much attention from administrative agencies recently because some case studies have achieved compelling results.Moreover, we have implemented two types of application interface: a Web application interface and an android application interface.

View Article: PubMed Central - PubMed

Affiliation: Graduate School of Information Sciences, Hiroshima City University, 3-4-1, Ozuka-Higashi, Asa-Minami-Ku, 731-3194 Hiroshima Japan.

ABSTRACT
Since social media started getting more attention from users on the Internet, social media has been one of the most important information source in the world. Especially, with the increasing popularity of social media, data posted on social media sites are rapidly becoming collective intelligence, which is a term used to refer to new media that is displacing traditional media. In this paper, we focus on geotagged tweets on the Twitter site. These geotagged tweets are referred to as georeferenced documents because they include not only a short text message, but also the documents' posting time and location. Many researchers have been tackling the development of new data mining techniques for georeferenced documents to identify and analyze emergency topics, such as natural disasters, weather, diseases, and other incidents. In particular, the utilization of geotagged tweets to identify and analyze natural disasters has received much attention from administrative agencies recently because some case studies have achieved compelling results. In this paper, we propose a novel real-time analysis application for identifying bursty local areas related to emergency topics. The aim of our new application is to provide new platforms that can identify and analyze the localities of emergency topics. The proposed application is composed of three core computational intelligence techniques: the Naive Bayes classifier technique, the spatiotemporal clustering technique, and the burst detection technique. Moreover, we have implemented two types of application interface: a Web application interface and an android application interface. To evaluate the proposed application, we have implemented a real-time weather observation system embedded the proposed application. we used actual crawling geotagged tweets posted on the Twitter site. The weather observation system successfully detected bursty local areas related to observed emergency weather topics.

No MeSH data available.