An improved framework for content and link-based web spam detection: a combined approach

In the modern digital era, the Web has been utilized for searching information by using different search engines (SE) as a tool. However, web spammers misuse the web for financial benefits by ranking the irrelevant and spam web pages higher than relevant pages in the search engine's results pages (SERPs) by using web spamming techniques. Furthermore, those top-ranked unrelated web pages contain insufficient or inappropriate information for the user. In addition, web spamming techniques dramatically affect the quality of the search engine. Researchers introduced several web spam detection techniques such as content-based features, link-based features, label propagation, label refinement, click-based web spamming detection, and real-time web spam detection. However, identifying all spam pages on the Web with high accuracy is still remains unsolved. This work proposes a content-based web spam detection framework, link-based web spam detection framework, and a combined approach to identify both types of web spams with high accuracy that can detect the newly evolved link pyramid. The content-based web spam detection framework uses three proposed and two improved content-based algorithms for web spam detection. The link-based web spam detection framework initially exposed the relationship network behind the link spamming and then used the paid-links database algorithm, spam signals algorithm, and improved link farms algorithm for link-based web spam identification. Finally, the combination of both content and link-based frameworks enhance the accuracy of web spam detection. The proposed combined approach's performance has been evaluated and compared with the J48 classifier, C4.5 decision tree classifier, SVM classifier, and heuristic combined approach. Some experiments were conducted to obtain the threshold values using the proposed collection architecture on well-known datasets WEB SPAM-UK2006 and WEB SPAM-UK2007. The results show that the proposed methods outperform other methods with 82.1% precision and an F-measure of 80.6% to illustrate the proposed framework's effectiveness and applicability

An integrated ranking algorithm for efficient information computing in social networks

Social networks have ensured the expanding disproportion between the face of WWW stored traditionally in search engine repositories and the actual ever changing face of Web. Exponential growth of web users and the ease with which they can upload contents on web highlights the need of content controls on material published on the web. As definition of search is changing, socially-enhanced interactive search methodologies are the need of the hour. Ranking is pivotal for efficient web search as the search performance mainly depends upon the ranking results. In this paper new integrated ranking model based on fused rank of web object based on popularity factor earned over only valid interlinks from multiple social forums is proposed. This model identifies relationships between web objects in separate social networks based on the object inheritance graph. Experimental study indicates the effectiveness of proposed Fusion based ranking algorithm in terms of better search results.Comment: 14 pages, International Journal on Web Service Computing (IJWSC), Vol.3, No.1, March 201

Role of Ranking Algorithms for Information Retrieval

As the use of web is increasing more day by day, the web users get easily lost in the web's rich hyper structure. The main aim of the owner of the website is to give the relevant information according their needs to the users. We explained the Web mining is used to categorize users and pages by analyzing user's behavior, the content of pages and then describe Web Structure mining. This paper includes different Page Ranking algorithms and compares those algorithms used for Information Retrieval. Different Page Rank based algorithms like Page Rank (PR), WPR (Weighted Page Rank), HITS (Hyperlink Induced Topic Selection), Distance Rank and EigenRumor algorithms are discussed and compared. Simulation Interface has been designed for PageRank algorithm and Weighted PageRank algorithm but PageRank is the only ranking algorithm on which Google search engine works.Comment: Keywords: Page Rank, Web Mining, Web Structured Mining, Web Content Minin

Social Search with Missing Data: Which Ranking Algorithm?

Online social networking tools are extremely popular, but can miss potential discoveries latent in the social 'fabric'. Matchmaking services which can do naive profile matching with old database technology are too brittle in the absence of key data, and even modern ontological markup, though powerful, can be onerous at data-input time. In this paper, we present a system called BuddyFinder which can automatically identify buddies who can best match a user's search requirements specified in a term-based query, even in the absence of stored user-profiles. We deploy and compare five statistical measures, namely, our own CORDER, mutual information (MI), phi-squared, improved MI and Z score, and two TF/IDF based baseline methods to find online users who best match the search requirements based on 'inferred profiles' of these users in the form of scavenged web pages. These measures identify statistically significant relationships between online users and a term-based query. Our user evaluation on two groups of users shows that BuddyFinder can find users highly relevant to search queries, and that CORDER achieved the best average ranking correlations among all seven algorithms and improved the performance of both baseline methods

Improving the evaluation of web search systems

Linkage analysis as an aid to web search has been assumed to be of significant benefit and we know that it is being implemented by many major Search Engines. Why then have few TREC participants been able to scientifically prove the benefits of linkage analysis over the past three years? In this paper we put forward reasons why disappointing results have been found and we identify the linkage density requirements of a dataset to faithfully support experiments into linkage analysis. We also report a series of linkage-based retrieval experiments on a more densely linked dataset culled from the TREC web documents