Retrieval and novelty detection at the sentence level
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Trend detection through temporal link analysis
Journal of the American Society for Information Science and Technology - Special issue: Webometrics
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Similarity measures for tracking information flow
Proceedings of the 14th ACM international conference on Information and knowledge management
What's really new on the web?: identifying new pages from a series of unstable web snapshots
Proceedings of the 15th international conference on World Wide Web
A comparison of sentence retrieval techniques
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Using neighbors to date web documents
Proceedings of the 9th annual ACM international workshop on Web information and data management
Proceedings of the 9th annual ACM international workshop on Web information and data management
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Topic initiator detection on the world wide web
Proceedings of the 19th international conference on World wide web
Carbon dating the web: estimating the age of web resources
Proceedings of the 22nd international conference on World Wide Web companion
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As the same information appears on many Web pages, we often want to know which page is the first one that discussed it, or how the information has spread on the Web as time passes. In this paper, we develop two methods: a method of detecting the first page that discussed the given information, and a method of generating a graph showing how the number of pages discussing it has changed along the timeline. To extract such information, we need to determine which pages discuss the given topic, and also need to determine when these pages were created. For the former step, we design a metric for estimating inclusion degree between information and a page. For the latter step, we develop a technique of extracting creation timestamps on web pages. Although timestamp extraction is a crucial component in temporal Web analysis, no research has shown how to do it in detail. Both steps are, however, still error-prone. In order to improve noise elimination, we examine not only the properties of each page, but also temporal relationship between pages. If temporal relationship between some candidate page and other pages are unlikely in typical patterns of information spread on the Web, we eliminate the candidate page as a noise. Results of our experiments show that our methods achieve high precision and can be used for practical use.