1,401 Hits in 3.8 sec

Detection of Fake Online Reviews using ML

Mallikarjuna S B
2020 International Journal for Research in Applied Science and Engineering Technology  
This paper presents a few semi-supervised and supervised content mining models to recognize counterfeit online audits just as analyses the productivity of both procedures on dataset containing lodging  ...  Basic leadership for acquisition of on the web items generally relies upon surveys given by the clients. Thus, shrewd people or gatherings attempt to control item surveys for their own advantages.  ...  This is one of the major drawbacks of the supervised learning methods and to mitigate the effects of these weaknesses we explore a semi supervised approach for spam detection based on a co-training algorithm  ... 
doi:10.22214/ijraset.2020.30950 fatcat:7aq4mkyq2nhyvmp54qgah6xpui

Link based small sample learning for web spam detection

Guang-Gang Geng, Qiudan Li, Xinchang Zhang
2009 Proceedings of the 18th international conference on World wide web - WWW '09  
Robust statistical learning based web spam detection system often requires large amounts of labeled training data.  ...  This paper proposed link based semi-supervised learning algorithms to boost the performance of a classifier, which integrates the traditional Self-training with the topological dependency based link learning  ...  CONCLUSION In this paper, we proposed two link-based semi-supervised learning algorithms to detect web spam on small labeled samples set.  ... 
doi:10.1145/1526709.1526920 dblp:conf/www/GengLZ09 fatcat:m33lw2g4bvdwbheqjbdvmzzvhq

Web Spam Detection by Learning from Small Labeled Samples

Jaber Karimpour, Ali A. Noroozi, Somayeh Alizadeh
2012 International Journal of Computer Applications  
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.  ...  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.  ...  Other semi-supervised learning algorithms like Co-training can be used and compared with the EM algorithm.  ... 
doi:10.5120/7924-0993 fatcat:sv2emqccx5blfj5wlytcy6ffl4

Analyzing the effectiveness of semi-supervised learning approaches for opinion spam classification

Alexander Ligthart, Cagatay Catal, Bedir Tekinerdogan
2021 Applied Soft Computing  
To reduce this increasing impact of opinion spams, opinion spam detection approaches have been proposed, which adopt mostly supervised classification methods.  ...  According to this study, the self-training algorithm with Naive Bayes as the base classifier yields 93% accuracy.  ...  [34] developed a new model based on semi-supervised recursive autoencoders for spam review detection.  ... 
doi:10.1016/j.asoc.2020.107023 fatcat:equp6saycnexrj4c643unlhcoq

A co-classification framework for detecting web spam and spammers in social media web sites

Feilong Chen, Pang-Ning Tan, Anil K. Jain
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
In this paper, we present a co-classification framework to detect Web spam and the spammers who are responsible for posting them on the social media Web sites.  ...  The rationale for our approach is that since both detection tasks are related, it would be advantageous to train them simultaneously to make use of the labeled examples in the Web spam and spammer training  ...  This paper presents a co-classification framework for Web spam and spammer detection in social media based on the maximum margin principle.  ... 
doi:10.1145/1645953.1646235 dblp:conf/cikm/ChenTJ09 fatcat:anbzuof3x5dzbikexvurtens2a


2016 International journal of computer and communication technology  
This paper previews and reviews the substantial research on Review Spam detection technique. Further it provides state of art depicting some previous attempt to study review spam detection.  ...  The proliferation of E-commerce sites has made web an excellent source of gathering customer reviews about products; as there is no quality control anyone one can write anything which leads to review spam  ...  A co-training algorithm was given with two views of feature set (review and reviewer based) for semi-supervised machine learning which are outlined below: Review based features: 1. content feature 2. sentiment  ... 
doi:10.47893/ijcct.2016.1332 fatcat:iqftdn5myffeflr2i4gje6nd4a

A Feature-Partition and Under-Sampling Based Ensemble Classifier for Web Spam Detection

Xiaoyong Lu, Musheng Chen, Jhenglong Wu, Peichan Chan
2015 International Journal of Machine Learning and Computing  
Web spam detection has become one of the top important tasks for web search engines. Web spam detection is a class imbalance problem because normal pages are far more than spam pages.  ...  Index Terms-Web spam detection, under-sampling, features partition, ensemble classifier, C4.5.  ...  ACKNOWLEDGMENT The authors wish to acknowledge Carlos Castillo, who has supported the WEBSPAM-UK2006 Corpus web site and helped us to download the collection.  ... 
doi:10.18178/ijmlc.2015.5.6.551 fatcat:ytkh6zymenc3hdlotehzaytyza

Removing Spam from Web Corpora Through Supervised Learning and Semi-manual Classification of Web Sites

Vít Suchomel
2020 Recent Advances in Slavonic Natural Languages Processing  
Then, a semi-manual approach of obtaining samples of non-text web pages in Estonian is introduced. This strategy makes the supervised learning process more efficient.  ...  Internet spam is a major issue hindering the usefulness of web corpora. Unlike traditional text corpora collected from trustworthy sources, the content of web based corpora has to be cleaned.  ...  Removing Spam from English Web Corpus Through Supervised Learning This section describes training and evaluation of a supervised classifier to detect spam in web corpora.  ... 
dblp:conf/raslan/Suchomel20 fatcat:jnyc7i4hara2xj4sxg6cd37rqq

Online Fraud Review Detection Using Data Mining

2020 International journal for research in engineering application & management  
This paper introduces some semi-supervised and supervised text mining models to detect fake online reviews as well as compares the efficiency of both techniques on dataset containing hotel reviews.  ...  Opinion Spam detection is an exhausting and hard problem as there are many faux or fake reviews that have been created by organizations or by the people for various purposes.  ...  They proposed several semi-supervised learning techniques which includes Co-training, Expectation maximization, Label Propagation and Spreading and Positive Unlabelled Learning [8] .  ... 
doi:10.35291/2454-9150.2020.0416 fatcat:eo2ujzcfovdxndaqahuohc3444

Deceptive Review Spam Detection via Exploiting Task Relatedness and Unlabeled Data

Zhen Hai, Peilin Zhao, Peng Cheng, Peng Yang, Xiao-Li Li, Guangxia Li
2016 Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing  
We then propose a novel semi-supervised multitask learning method via Laplacian regularized logistic regression (SMTL-LLR) to further improve the review spam detection performance.  ...  We first develop a multi-task learning method based on logistic regression (MTL-LR), which can boost the learning for a task by sharing the knowledge contained in the training signals of other related  ...  Li et al. (2011) used a two-view co-training method (Blum and Mitchell, 1998) for semi-supervised learning to identify fake review spam.  ... 
doi:10.18653/v1/d16-1187 dblp:conf/emnlp/HaiZCYLL16 fatcat:cxhnjz2b6jafhnmxahz7s4o5h4

A Domain-Agnostic Approach to Spam-URL Detection via Redirects [chapter]

Heeyoung Kwon, Mirza Basim Baig, Leman Akoglu
2017 Lecture Notes in Computer Science  
In this work we propose a new approach for detecting spam URLs on the Web. Our key idea is to leverage the properties of URL redirections widely deployed by spammers.  ...  difficulty, risk, or cost on spammers to evade as it is tightly coupled with their operational behavior, and (3) semi-supervised detection, which uses only a few labeled examples to produce competitive  ...  In this work, we study both supervised and semi-supervised detection, with a note that the latter presents a more realistic scenario. Supervised Detection.  ... 
doi:10.1007/978-3-319-57529-2_18 fatcat:45il5vag4feebfazyspn7azqoa

Spam Detection using Sentiment Analysis of Text

Vignesh N
2019 International Journal for Research in Applied Science and Engineering Technology  
still barely detect spam reviews, and none of them show the importance of each extracted feature type.  ...  In this study, we propose a novel framework, named NetSpam, which utilizes spam features for modeling review datasets as heterogeneous information networks to map spam detection procedure into a classification  ...  Based on this observation, we provide a twoview semi-supervised method, co-training, to exploit the large amount of unlabeled data. The experiment results show that our proposed method is effective.  ... 
doi:10.22214/ijraset.2019.3413 fatcat:fpu7clgop5b7nmqt6pcaf4y5gi


Saini Jacob Soman, S. Murugappan
2014 Journal of Computer Science  
Therefore, this study attempts to review various spam detection frameworks which deals about the detection and elimination of spams in various sources.  ...  Spam pervades any information system such as e-mail or web, social, blog or reviews platform.  ...  Here, spam comment is an irrelevant response received for a blog post in the form of a comment. This comments are analyzed using supervised and semi supervised methods.  ... 
doi:10.3844/jcssp.2014.2135.2140 fatcat:56u4f2hs75gdbl2w5yjyoagfym

A Study of Spam Detection Algorithm on Social Media Networks [chapter]

Jacob Soman Saini
2013 Advances in Intelligent Systems and Computing  
Therefore, this study attempts to review various spam detection frameworks which deals about the detection and elimination of spams in various sources.  ...  Spam pervades any information system such as e-mail or web, social, blog or reviews platform.  ...  Here, spam comment is an irrelevant response received for a blog post in the form of a comment. This comments are analyzed using supervised and semi supervised methods.  ... 
doi:10.1007/978-81-322-1680-3_22 fatcat:zbmffnwkufabrp3d6a5dd444ei

Reducing the Cost of Training Security Classifier (via Optimized Semi-Supervised Learning) [article]

Rui Shu, Tianpei Xia, Huy Tu, Laurie Williams, Tim Menzies
2022 arXiv   pre-print
Most of the existing machine learning models for security tasks, such as spam detection, malware detection, or network intrusion detection, are built on supervised machine learning algorithms.  ...  Conclusion: Based on those results, we would recommend using hyperparameter optimization with semi-supervised learning when dealing with shortages of labeled security data.  ...  For example, in Twitter spam detection [9] , machine learning algorithms use account-based features (e.g., the number of followers or friends) or message-based features (e.g., length of a tweet) to train  ... 
arXiv:2205.00665v1 fatcat:ss6bh2t5mbeajpzietivchugeu
« Previous Showing results 1 — 15 out of 1,401 results