Content-based analysis to detect Arabic web spam

2012 ◽  
Vol 38 (3) ◽  
pp. 284-296 ◽  
Author(s):  
Mohammed Al-Kabi ◽  
Heider Wahsheh ◽  
Izzat Alsmadi ◽  
Emad Al-Shawakfa ◽  
Abdullah Wahbeh ◽  
...  
Keyword(s):  
Decision Tree ◽  
Search Engines ◽  
Daily Basis ◽  
Classification Algorithms ◽  
Web Pages ◽  
Huge Amount ◽  
Dynamic Nature ◽  
Search Results ◽  
Web Spam ◽  
The Web

Search engines are important outlets for information query and retrieval. They have to deal with the continual increase of information available on the web, and provide users with convenient access to such huge amounts of information. Furthermore, with this huge amount of information, a more complex challenge that continuously gets more and more difficult to illuminate is the spam in web pages. For several reasons, web spammers try to intrude in the search results and inject artificially biased results in favour of their websites or pages. Spam pages are added to the internet on a daily basis, thus making it difficult for search engines to keep up with the fast-growing and dynamic nature of the web, especially since spammers tend to add more keywords to their websites to deceive the search engines and increase the rank of their pages. In this research, we have investigated four different classification algorithms (naïve Bayes, decision tree, SVM and K-NN) to detect Arabic web spam pages, based on content. The three groups of datasets used, with 1%, 15% and 50% spam contents, were collected using a crawler that was customized for this study. Spam pages were classified manually. Different tests and comparisons have revealed that the Decision Tree was the best classifier for this purpose.

Cluster Search Engine Results with Crowd Intelligence

2013 ◽  
Vol 284-287 ◽  
pp. 3375-3379
Author(s):  
Chun Hsiung Tseng ◽  
Fu Cheng Yang ◽  
Yu Ping Tseng ◽  
Yi Yun Chang
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Web Pages ◽  
Huge Amount ◽  
Search Results ◽  
Web Search Engines ◽  
Crowd Intelligence ◽  
User Queries

Most Web users today rely heavily on search engines to gather information. To achieve better search results, some algorithms such as PageRank have been developed. However, most Web search engines employ keyword-based search and thus have some natural weaknesses. Among these problems, a well-known one is that it is very difficult for search engines to infer semantics from user queries and returned results. Hence, despite of efforts of ranking search results, users may still have to navigate through a huge amount of Web pages to locate the desired resources. In this research, the researchers developed a clustering-based methodology to improve the performance of search engines. Instead of extracting features used for clustering from the returned documents, the proposed method extracts features from the delicious service, which is actually a tag provider service. By utilizing such information, the resulting system can benefit from crowd intelligence. The obtained information is then used for enhancing the performance of the ordinary k-means algorithm to achieve better clustering results.

scholarly journals Effective Search Engine Spam Classification

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1541-1545
Keyword(s):  
Decision Tree ◽  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Web Pages ◽  
Classification Methods ◽  
Search Results ◽  
Web Search Engine ◽  
Engine Industry

Search engine spam is formed by the spam creators for commercial gain. Spammers applied different strategies in web pages to display the first page of web search results. These strategies may avoid displaying good quality web pages in the top of search engine results page. Nowadays there are numerous devised algorithms available to identify search engine spam. Even though search engines are still affected by search engine spam. There is a necessity for search engine industry to filter search engine spam in the best way. The proposed study identifies spam in web search engine. Spammers try to use most popular search keywords, popular links and advertising keywords in web pages. This strategy helps to increase ranking to display the top of search results. The proposed method is used important features to detect spam pages which are classified using decision tree C4.5 classifier. This method produces better performance when compared with existing classification methods.

A Roadmap to Integrate Document Clustering in Information Retrieval

2013 ◽  
pp. 31-45
Author(s):  
R. Subhashini ◽  
V.Jawahar Senthil Kumar
Keyword(s):  
Information Retrieval ◽  
Search Engines ◽  
World Wide ◽  
Clustering Algorithm ◽  
Web Search ◽  
Full Potential ◽  
Digital Information ◽  
Search Results ◽  
The World ◽  
The Web

The World Wide Web is a large distributed digital information space. The ability to search and retrieve information from the Web efficiently and effectively is an enabling technology for realizing its full potential. Information Retrieval (IR) plays an important role in search engines. Today’s most advanced engines use the keyword-based (“bag of words”) paradigm, which has inherent disadvantages. Organizing web search results into clusters facilitates the user’s quick browsing of search results. Traditional clustering techniques are inadequate because they do not generate clusters with highly readable names. This paper proposes an approach for web search results in clustering based on a phrase based clustering algorithm. It is an alternative to a single ordered result of search engines. This approach presents a list of clusters to the user. Experimental results verify the method’s feasibility and effectiveness.

User Models for Adaptive Information Retrieval on the Web

2012 ◽  
Vol 3 (3) ◽  
pp. 1-19
Author(s):  
Max Chevalier ◽  
Christine Julien ◽  
Chantal Soulé-Dupuy
Keyword(s):  
Information Retrieval ◽  
Search Engines ◽  
User Profile ◽  
User Model ◽  
User Models ◽  
Search Results ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Adaptive Information Retrieval ◽  
The Web

Searching information can be realized thanks to specific tools called Information Retrieval Systems IRS (also called “search engines”). To provide more accurate results to users, most of such systems offer personalization features. To do this, each system models a user in order to adapt search results that will be displayed. In a multi-application context (e.g., when using several search engines for a unique query), personalization techniques can be considered as limited because the user model (also called profile) is incomplete since it does not exploit actions/queries coming from other search engines. So, sharing user models between several search engines is a challenge in order to provide more efficient personalization techniques. A semantic architecture for user profile interoperability is proposed to reach this goal. This architecture is also important because it can be used in many other contexts to share various resources models, for instance a document model, between applications. It is also ensuring the possibility for every system to keep its own representation of each resource while providing a solution to easily share it.

scholarly journals Distributed and collaborative Web Change Detection system

2015 ◽  
Vol 12 (1) ◽  
pp. 91-114 ◽  
Author(s):  
Víctor Prieto ◽  
Manuel Álvarez ◽  
Víctor Carneiro ◽  
Fidel Cacheda
Keyword(s):  
Change Detection ◽  
Search Engines ◽  
Web Site ◽  
Detection System ◽  
Computational Cost ◽  
Web Pages ◽  
Web Page ◽  
Case Scenario ◽  
Worst Case ◽  
The Web

Search engines use crawlers to traverse the Web in order to download web pages and build their indexes. Maintaining these indexes up-to-date is an essential task to ensure the quality of search results. However, changes in web pages are unpredictable. Identifying the moment when a web page changes as soon as possible and with minimal computational cost is a major challenge. In this article we present the Web Change Detection system that, in a best case scenario, is capable to detect, almost in real time, when a web page changes. In a worst case scenario, it will require, on average, 12 minutes to detect a change on a low PageRank web site and about one minute on a web site with high PageRank. Meanwhile, current search engines require more than a day, on average, to detect a modification in a web page (in both cases).

Visual comparison of search results: A censorship case study

First Monday ◽  
2008 ◽  
Author(s):  
Mark Meiss ◽  
Filippo Menczer
Keyword(s):  
User Interface ◽  
Search Engines ◽  
Web Pages ◽  
Search Results ◽  
Visual Comparison

Understanding the qualitative differences between the sets of results from different search engines can be a difficult task. How many links must you follow from each list before you can reach a conclusion? We describe a user interface that allows users to quickly identify the most significant differences in content between two lists of Web pages. We have implemented this interface in CenSEARCHip, a system for comparing the effects of censorship policies on search engines.

What is popular on Wikipedia and why?

First Monday ◽  
2007 ◽  
Author(s):  
Anselm Spoerri
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Search Behavior ◽  
Search Queries ◽  
Search Results ◽  
The Web

This paper analyzes which pages and topics are the most popular on Wikipedia and why. For the period of September 2006 to January 2007, the 100 most visited Wikipedia pages in a month are identified and categorized in terms of the major topics of interest. The observed topics are compared with search behavior on the Web. Search queries, which are identical to the titles of the most popular Wikipedia pages, are submitted to major search engines and the positions of popular Wikipedia pages in the top 10 search results are determined. The presented data helps to explain how search engines, and Google in particular, fuel the growth and shape what is popular on Wikipedia.

scholarly journals The Canon of Dutch Literature According to Google

2019 ◽  
Author(s):  
Lucas van der Deijl ◽  
Antal van den Bosch ◽  
Roel Smeets
Keyword(s):  
Knowledge Base ◽  
Search Engines ◽  
Literary History ◽  
Information Sources ◽  
Search Algorithms ◽  
Web Pages ◽  
Printed Media ◽  
Literary Histories ◽  
Dutch Literature ◽  
The Web

Literary history is no longer written in books alone. As literary reception thrives in blogs, Wikipedia entries, Amazon reviews, and Goodreads pro les, the Web has become a key platform for the exchange of information on literature. Al- though conventional printed media in the eld—academic monographs, literary supplements, and magazines—may still claim the highest authority, online me- dia presumably provide the rst (and possibly the only) source for many readers casually interested in literary history. Wikipedia o ers quick and free answers to readers’ questions and the range of topics described in its entries dramatically exceeds the volume any printed encyclopedia could possibly cover. While an important share of this expanding knowledge base about literature is produced bottom-up (user based and crowd-sourced), search engines such as Google have become brokers in this online economy of knowledge, organizing information on the Web for its users. Similar to the printed literary histories, search engines prioritize certain information sources over others when ranking and sorting Web pages; as such, their search algorithms create hierarchies of books, authors, and periods.

A Study on Web Searching

2004 ◽  
pp. 208-225
Author(s):  
Shanfeng Zhu ◽  
Xiaotie Deng ◽  
Qizhi Fang ◽  
Weimin Zhang
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Experimental Results ◽  
Web Searching ◽  
Search Results ◽  
Total Index ◽  
Depth Study ◽  
Web Search Engines ◽  
The Web

Web search engines are one of the most popular services to help users find useful information on the Web. Although many studies have been carried out to estimate the size and overlap of the general web search engines, it may not benefit the ordinary web searching users, since they care more about the overlap of the top N (N=10, 20 or 50) search results on concrete queries, but not the overlap of the total index database. In this study, we present experimental results on the comparison of the overlap of the top N (N=10, 20 or 50) search results from AlltheWeb, Google, AltaVista and WiseNut for the 58 most popular queries, as well as for the distance of the overlapped results. These 58 queries are chosen from WordTracker service, which records the most popular queries submitted to some famous metasearch engines, such as MetaCrawler and Dogpile. We divide these 58 queries into three categories for further investigation. Through in-depth study, we observe a number of interesting results: the overlap of the top N results retrieved by different search engines is very small; the search results of the queries in different categories behave in dramatically different ways; Google, on average, has the highest overlap among these four search engines; each search engine tends to adopt a different rank algorithm independently.

Clustering of the Web Search Results in Educational Recommender Systems

2012 ◽  
pp. 154-181 ◽  
Author(s):  
Constanta-Nicoleta Bodea ◽  
Maria-Iuliana Dascalu ◽  
Adina Lipai
Keyword(s):  
Recommender Systems ◽  
Clustering Algorithm ◽  
Web Search ◽  
Web Pages ◽  
Lexical Database ◽  
Assessment Task ◽  
Search Results ◽  
Meta Search ◽  
Search Approach ◽  
The Web

This chapter presents a meta-search approach, meant to deliver bibliography from the internet, according to trainees’ results obtained at an e-assessment task. The bibliography consists of web pages related to the knowledge gaps of the trainees. The meta-search engine is part of an education recommender system, attached to an e-assessment application for project management knowledge. Meta-search means that, for a specific query (or mistake made by the trainee), several search mechanisms for suitable bibliography (further reading) could be applied. The lists of results delivered by the standard search mechanisms are used to build thematically homogenous groups using an ontology-based clustering algorithm. The clustering process uses an educational ontology and WordNet lexical database to create its categories. The research is presented in the context of recommender systems and their various applications to the education domain.

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