Abstract
Nowadays, Web makes it possible to study emergencies from web information due to its real-time, open, and dynamic features. After the emergence of a web event, there will be numerous websites publishing webpages to cover this web event. Measuring temporal features in evolution course of web events can help people timely know and understand which events are emergencies, so harms to the society caused by emergencies can be reduced. In this paper, website preference is formally defined and mined by three proposed strategies which are all explicitly or implicitly based on the three-level networks: website-level, webpage-level and keyword-level. An iterative algorithm is firstly introduced to calculate outbreak power of web events, and increased web pages of events, increased attributes of events, distribution of attributes in web pages and the relationships of attributes are embedded into this iterative algorithm as the variables. 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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Acknowledgements
This work was supported in part by the National Science and Technology Major Project under Grant 2013ZX01033002-003, in part by the National High Technology Research and Development Program of China (863 Program) under Grant 2013AA014603, in part by the National Science Foundation of China under Grant 61300202, in part by the China Postdoctoral Science Foundation under Grant 2014M560085, and in part by the Science Foundation of Shanghai under Grant 13ZR1452900.
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Xu, Z., Xuan, J., Zhu, Y., Wei, X. (2018). Building the Profile of Web Events Based on Website Measurement. In: Yen, N., Hung, J. (eds) Frontier Computing. FC 2016. Lecture Notes in Electrical Engineering, vol 422. Springer, Singapore. https://doi.org/10.1007/978-981-10-3187-8_1
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DOI: https://doi.org/10.1007/978-981-10-3187-8_1
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