IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering trends in software engineering with google trend
ACM SIGSOFT Software Engineering Notes
A knowledge management system for series-parallel availability optimization and design
Expert Systems with Applications: An International Journal
Structure and Network in the YouTube Core
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Looking at, looking up or keeping up with people?: motives and use of facebook
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Interpreting TF-IDF term weights as making relevance decisions
ACM Transactions on Information Systems (TOIS)
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Analyzing the impact of events in an online music community
Proceedings of the 4th Workshop on Social Network Systems
Total recall II: Query expansion revisited
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
What issue spread on the web: analyze the web trends
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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Social networking services have received a lot of attention recently so that the discussion of certain issues is becoming more dynamic. Many websites provide a new service that displays the list of the trending social issues. It is very important to respond to those social issues since the impact on organisations or people may be considerable. In this paper, we present our research on developing the personalised relevance identification system that displays the relevance of social issues to a target domain. To accomplish this, we first collected social issue keywords from Google Trends, Twitter and Google News. After that, we setup an electronic document management system as a target domain that would include all knowledge and activities having to do with a target object. In order to identify the relevance of the social issues to a target, we applied the Term Frequency Inverse Document Frequency (TFIDF). Our experiments prove that we can identify the meaningful relevance of social issues to targets, such as individuals or organizations.