An Improved Framework for Content-based Spamdexing Detection
| Autor: | Asim Shahzad, Hairulnizam Mahdin, Nazri Mohd |
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| Rok vydání: | 2020 |
| Předmět: |
Stop words
Information retrieval General Computer Science Computer science 030206 dentistry 02 engineering and technology Spamdexing 03 medical and health sciences Identification (information) Search engine 0302 clinical medicine 020204 information systems Web page 0202 electrical engineering electronic engineering information engineering |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 11 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2020.0110151 |
| Popis: | To the modern Search Engines (SEs), one of the biggest threats to be considered is spamdexing. Nowadays spammers are using a wide range of techniques for content generation, they are using content spam to fill the Search Engine Result Pages (SERPs) with low-quality web pages. Generally, spam web pages are insufficient, irrelevant and improper results for users. Many researchers from academia and industry are working on spamdexing to identify the spam web pages. However, so far not even a single universally efficient method is developed for identification of all spam web pages. We believe that for tackling the content spam there must be improved methods. This article is an attempt in that direction, where a framework has been proposed for spam web pages identification. The framework uses Stop words, Keywords Density, Spam Keywords Database, Part of Speech (POS) ratio, and Copied Content algorithms. For conducting the experiments and obtaining threshold values WEBSPAM-UK2006 and WEBSPAM-UK2007 datasets have been used. An excellent and promising F-measure of 77.38% illustrates the effectiveness and applicability of proposed method. |
| Databáze: | OpenAIRE |
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