TY - GEN
T1 - It is time to prepare for the future
T2 - Computer Applications for Database, Education and Ubiquitous Computing
AU - Han, Soyeon Caren
AU - Chung, Hyunsuk
AU - Kang, Byeong Ho
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2012
Y1 - 2012
N2 - A social issue is what arises when the public discuss a specific event. Recently, many large Internet based service companies provide new trends services that display the emerging issues based on their data, for example, Google displays "top 10 most searched topics" every hour. Those emerging issues reflect the trend of public interest. Forecasting those issues helps the user to prepare for the future. In this paper, we present our research on proposing the social issue-forecasting model. To do so, we first collected social issue keyword from Google Trends for 3 months since it is based on the large scale of public data. We apply the k-nearest neighbor algorithm, which is the pattern recognition technology for recognizing the complex patterns and trends. To improve the accuracy, we applied Ripple Down Rules.
AB - A social issue is what arises when the public discuss a specific event. Recently, many large Internet based service companies provide new trends services that display the emerging issues based on their data, for example, Google displays "top 10 most searched topics" every hour. Those emerging issues reflect the trend of public interest. Forecasting those issues helps the user to prepare for the future. In this paper, we present our research on proposing the social issue-forecasting model. To do so, we first collected social issue keyword from Google Trends for 3 months since it is based on the large scale of public data. We apply the k-nearest neighbor algorithm, which is the pattern recognition technology for recognizing the complex patterns and trends. To improve the accuracy, we applied Ripple Down Rules.
KW - Google Trends
KW - Social Issue forecasting
KW - Social Networking Sites
KW - Web Trends Forecasting
UR - http://www.scopus.com/inward/record.url?scp=84870948061&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-35603-2_48
DO - 10.1007/978-3-642-35603-2_48
M3 - Conference paper
SN - 9783642356025
T3 - Communications in Computer and Information Science
SP - 325
EP - 331
BT - Computer Applications for Database, Education, and Ubiquitous Computing
PB - Springer
CY - Cham
Y2 - 16 December 2012 through 19 December 2012
ER -