OUTBREAK POWER MEASUREMENT FOR EVOLUTION COURSE OF WEB EVENTS
Keywords:emergent events, web events, semantic measure, web mining, fuzzy pattern recognition
Nowadays, emergencies have a great impact on people’s daily lives. Web makes it possible to study emergencies from web information due to its real-time, open, and dynamic features. Measuring temporal features in web events evolution course can help people timely get knowledge and understand emergent events, which contribute to reducing harms to our society caused by emergencies. In this paper, we propose an outbreak power measuring algorithm for the evolution of web events, in order to provide guidance for automatic detection and prediction of emergencies. An iterative algorithm is firstly introduced to calculate outbreak power of web events through increased web pages of events, increased attributes of events, and distribution of attributes in web pages and the relationships of attributes. Secondly, definition of web events types is proposed. From studying each type of web events, we dig out feature patterns and find laws of each type events, with hot event having the highest outbreak power while general event have the lowest outbreak power, and general event fluctuating most while urgent event fluctuating least, which can be prior knowledge of web events we study. And then, a fuzzy based algorithm is presented to discriminate the type of web events. By means of prior knowledge, membership grade of web events belong to each type can be calculated, and then the type of web events can be discriminated. Experiments on real data set demonstrate the proposed algorithm is both efficient and effective, and it is capable of providing accurate results of discrimination.
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