A Model to Detect Keyword Stuffing Spam on Webpages
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© 2023 by IJCTT Journal
Volume-71 Issue-3
Year of Publication : 2023
Authors : Bodunde Odunola Akinyemi
DOI :  10.14445/22312803/IJCTT-V71I3P103

How to Cite?

Bodunde Odunola Akinyemi, "A Model to Detect Keyword Stuffing Spam on Webpages," International Journal of Computer Trends and Technology, vol. 71, no. 3, pp. 14-20, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I3P103

Abstract
A well-designed website's dominant point and success depend on using keywords. Search engines heavily depend on the concept of keyword analysis to highlight results for search queries on web pages and to establish highly ranked websites. However, keyword stuffing evokes a spam issue with regard to the relevance of the content, so it becomes imperative that appropriate keywords are used to optimise web pages. This study developed a spam detection model to address the problem of keyword stuffing on a webpage. The model was developed by integrating three content analysis detection techniques: rates of compression ratio, average length, and keyword density. The Python programming language was used to implement the proposed approach. To evaluate the model's performance, twenty webpages were selected, out of which the contents of five sites were altered by including more keywords than usual. A simulation of the proposed model was tested on each webpage before and after the alteration of the keywords. The findings showed that before and after manipulation, the edited five sites' average identified keywords ranged from 2% to 3%. According to the results of the density of the pages’ analysis, the average page density ranged from 3% to 5%. The study concluded that a keyword stuffing evaluation and detection model for webpages must be established to prevent online users from being misled and to increase trust between users and search engines.

Keywords
Content-based, Keyword density, Keyword Stuffing, Spam, Webpages.

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