Noise robust detection of the emergence and spread of topics on the web

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.