| Title |
Real-time analysis application for identifying bursty local areas related to emergency topics
|
|---|---|
| Published in |
SpringerPlus, April 2015
|
| DOI | 10.1186/s40064-015-0817-x |
| Pubmed ID | |
| Authors |
Tatsuhiro Sakai, Keiichi Tamura |
| 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. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 20% |
| Japan | 1 | 20% |
| United States | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Philippines | 1 | 2% |
| Ireland | 1 | 2% |
| Unknown | 39 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 24% |
| Researcher | 7 | 17% |
| Student > Master | 7 | 17% |
| Student > Bachelor | 5 | 12% |
| Librarian | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 6 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 29% |
| Medicine and Dentistry | 6 | 15% |
| Environmental Science | 5 | 12% |
| Nursing and Health Professions | 3 | 7% |
| Business, Management and Accounting | 2 | 5% |
| Other | 7 | 17% |
| Unknown | 6 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 May 2015.
All research outputs
#7,400,888
of 22,803,211 outputs
Outputs from SpringerPlus
#484
of 1,851 outputs
Outputs of similar age
#89,683
of 264,242 outputs
Outputs of similar age from SpringerPlus
#17
of 61 outputs
Altmetric has tracked 22,803,211 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 1,851 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 264,242 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.