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Web spamming has tremendously subverted the ranking mechanism of information retrieval in Web search engines. It manipulates data source maliciously either by contents or links with the intention of contributing negative impacts to Web search results. The altering order of the search results by spammers has increased the difficulty level of searching and time consumption for Web users to retrieve relevant information. In order to improve the quality of Web search engines results, the design of anti-Web spam technique is developed to detect Web spam using Artificial Neural Network. This alternative machine learning classifier namely multilayered perceptron neural network further improve the detection rate of Web spam. In the experiments, the detection rate of Web spam using multilayered perceptron neural network has increased up to 14.02% and 3.53% over the conventional classifier support vector machines. At the same time, a mechanism to determine the number of hidden neurons for multilayered perceptron neural network is presented to simplify the designing process of network structure.
About the author
Goh Kwang Leng, Alex - Master of Philosophy and Bachelor of Science in Computer Science at Curtin University. Research Assistant at Curtin University, Sarawak Campus, Malaysia.
