Web Spam Detection by Learning from Small Labeled Samples

Abstract

Web spamming tries to deceive search engines to rank some pages higher than they deserve. Many methods have been proposed to combat web spamming and to detect spam pages. One basic method is using classification, i.e., learning a classification model from previously labeled training data and using this model for classifying web pages to spam or nonspam. A drawback of this method is that manually labeling a large number of web pages to generate the training data can be biased, non-accurate, labor intensive and time consuming. In this paper, we are going to propose a new method to resolve this drawback by using semi-supervised learning to automatically label the training data. To do this, we incorporate Expectation-Maximization algorithm that is an efficient and an important algorithm of semi-supervised learning. Experiments are carried out on the real web spam data, which show the new method, performs very well in practice.

Trust and Engagement on Twitter During the Management of COVID-19 Pandemic: The Effect of Gender and Position

“…To automatically determine the class labels (trust/distrust) of unlabelled response tweets, we adopted an expectation-maximization (EM) based semisupervised classifier (Nigam et al, 2006 ; Karimpour et al, 2012 ). EM is an iterative algorithm to maximize a posteriori estimation in datasets with both labeled and unlabeled data (Nigam et al, 2000 ).…”

Detection of Fake Online Reviews using Semi-supervised and Supervised learning

“…Out of 627 product reviews, 10 r eviews are found to be abusive, a nd hence, removed, and 48 are fo und to be spam PILAKA ANUSHA [9] Hadoop 1 represents the description of the classifiers, datasets and results used from the research work. Based on research work most of them used the SVM and naive bayes classifier, for different types of datasets.…”