RESEARCH ARTICLE Analysis of Web Spam for Non-English Content: Toward More Effective Language- Based Classifiers Mansour Alsaleh*, Abdulrahman Alarifi King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia *
[email protected] a11111 Abstract Web spammers aim to obtain higher ranks for their web pages by including spam contents that deceive search engines in order to include their pages in search results even when they are not related to the search terms. Search engines continue to develop new web spam detection mechanisms, but spammers also aim to improve their tools to evade detection. In OPEN ACCESS this study, we first explore the effect of the page language on spam detection features and we demonstrate how the best set of detection features varies according to the page lan- Citation: Alsaleh M, Alarifi A (2016) Analysis of Web Spam for Non-English Content: Toward More guage. We also study the performance of Google Penguin, a newly developed anti-web Effective Language-Based Classifiers. PLoS ONE spamming technique for their search engine. Using spam pages in Arabic as a case study, 11(11): e0164383. doi:10.1371/journal. we show that unlike similar English pages, Google anti-spamming techniques are ineffective pone.0164383 against a high proportion of Arabic spam pages. We then explore multiple detection features Editor: Muhammad Khurram Khan, King Saud for spam pages to identify an appropriate set of features that yields a high detection accu- University, SAUDI ARABIA racy compared with the integrated Google Penguin technique. In order to build and evaluate Received: June 3, 2016 our classifier, as well as to help researchers to conduct consistent measurement studies, we Accepted: September 23, 2016 collected and manually labeled a corpus of Arabic web pages, including both benign and Published: November 17, 2016 spam pages.