Investigating the Impact Factors for Event Detection Using Web Search Engines

Abstract

Online popular events, which are constructed from news stories using the techniques of Topic Detection and Tracking (TDT), bring convenience to users who intend to see what is going on through the Internet. Recently, the web is becoming an important event information provider and poster due to its real-time, open, and dynamic features. However, it is difficult to detect events since the huge scale and dynamics of the internet. In this paper, we define the novel problem of investigating impact factors for event detection. We give the definitions of five impact factors including the number of increased web pages, the number of increased keywords, the number of communities, the average clustering coefficient, and the average similarities of web pages. These five impact factors contain statistic and content information of an event. Empirical experiments on real datasets including Google Zeitgeist and Google Trends show that that the number of web pages and the average clustering coefficient can be used to detect events. Some strategies integrating the number of web pages and the average clustering coefficient are also employed. The evaluations on real dataset show that the proposed function integrating the number of web pages and the average clustering coefficient can be used for event detection efficiently and correctly.