Adaptive evaluation model of web spam based on link relation

Abstract

The credibility evaluation of web pages is an important part of current network information mining. Generally, content‐based evaluation and link relationship‐based evaluation are used to detect web spam. This paper proposes an algorithm based on link relationship which adjusts the weight of the link object according to the credibility of the link object to calculate the credibility of the web page. Considering the different linking conditions, (i) low‐scoring web pages actively linking to high‐scoring web pages, resulting in the reduction of high‐scoring web pages score; (ii) in order to endorse the low‐score web page, high‐score web pages actively linking to the low‐score web pages, adjust the score transmission. Obviously, the proposed algorithm is a recursive model. To avoid the amplification of the error caused by the loop and different evaluation results caused by different initial nodes, this paper presents a novel method to adaptively adjust the weights of the links according to the link conditions, then proves the convergence of the algorithm. Finally, we validate the proposed algorithm on the public datasets, which is compared with other up‐to‐date algorithms under different experimental indexes. The results show that the proposed algorithm can effectively reduce the ranks of spam pages in all web pages and improve the detection efficiency of spam pages.

Online Detection and Infographic Explanation of Spam Reviews with Data Drift Adaptation

“…Spammers can be humans or bots dedicated to creating deceptive reviews (García-Méndez et al, 2022b;Hamida et al, 2022). In this context, spam detection is a critical task in online systems.Spam negatively impacts the user experience and the performance and security of the system (Wang et al, 2021).…”