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The information seekers use different search engines (SEs) such as Google, Bing, and Yahoo as tools to search for products, services, and information. However, web spamming is one of the most significant issues encountered by SEs because it dramatically affects the quality of SE results. Web spamming\u2019s economic impact is enormous because web spammers index massive free advertising data on SEs to increase the volume of web traffic on a targeted website. Spammers trick an SE into ranking irrelevant web pages higher than relevant web pages in the search engine results pages (SERPs) using different web\u2010spamming techniques. Consequently, these high\u2010ranked unrelated web pages contain insufficient or inappropriate information for the user. To detect the spam web pages, several researchers from industry and academia are working. No efficient technique that is capable of catching all spam web pages on the World Wide Web (WWW) has been presented yet. This research is an attempt to propose an improved framework for content\u2010 and link\u2010based web\u2010spam identification. The framework uses stopwords, keywords\u2019 frequency, part of speech (POS) ratio, spam keywords database, and copied\u2010content algorithms for content\u2010based web\u2010spam detection. For link\u2010based web\u2010spam detection, we initially exposed the relationship network behind the link\u2010based web spamming and then used the paid\u2010link database, neighbour pages, spam signals, and link\u2010farm algorithms. Finally, we combined all the content\u2010 and link\u2010based spam identification algorithms to identify both types of spam. To conduct experiments and to obtain threshold values, WEBSPAM\u2010UK2006 and WEBSPAM\u2010UK2007 datasets were used. A promising F\u2010measure of 79.6% with 81.2% precision shows the applicability and effectiveness of the proposed approach.<\/jats:p>","DOI":"10.1155\/2021\/6625739","type":"journal-article","created":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T01:05:11Z","timestamp":1637024711000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An Improved Framework for Content\u2010 and Link\u2010Based Web\u2010Spam Detection: A Combined Approach"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5650-3983","authenticated-orcid":false,"given":"Asim","family":"Shahzad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0685-7892","authenticated-orcid":false,"given":"Nazri Mohd","family":"Nawi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2589-4189","authenticated-orcid":false,"given":"Muhammad Zubair","family":"Rehman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1718-7038","authenticated-orcid":false,"given":"Abdullah","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"e_1_2_14_1_2","unstructured":"GyongyiZ.andGarcia-MolinaH. 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