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Deceptive Opinions Detection Using New Proposed Arabic Semantic Features

Amel Ziani, Nabiha Azizi, Didier Schwab, Djamel Zenakhra, Monther Aldwairi, Nassira Chekkai, Nawel Zemmal, Marwa Hadj Salah
2021 Procedia Computer Science  
But there is always a lack concerning the Arabic annotated resources specially for deception detection area as it is considered new research area.  ...  Therefore, we used the semi supervised SVM to overcome this problem by using the unlabeled data.  ...  Now to train and classify this semi-supervised classifier we need a perfect representation for the text reviews. For this reason, we have used two sets of features: statistical and semantic.  ... 
doi:10.1016/j.procs.2021.05.067 fatcat:v7emkjqcx5f5tgk2njtzwcckka

Opinion Spam Detection based on Annotation Extension and Neural Networks

Yuanchao Liu, Bo Pang
2019 Computer and Information Science  
We propose a novel approach in this paper to boost opinion spam detection performance by fully utilizing the existing labelled small-size dataset.  ...  Subsequently, we examine neural network scenarios on a newly extended dataset to learn the distributed representation.  ...  Ren et al. (2016) use a neural network model to study the document-level representation for detecting deceptive opinion spam.  ... 
doi:10.5539/cis.v12n2p87 fatcat:acg46idthnb5bg3g73ub27wcoy

Enhanced Topic-based Vector Space Model for semantics-aware spam filtering

Igor Santos, Carlos Laorden, Borja Sanz, Pablo G. Bringas
2012 Expert systems with applications  
Based upon this representation, we apply several well-known machine-learning models and show that the proposed method can detect the internal semantics of spam messages.  ...  In this paper, we explore the use of semantics in spam filtering by representing e-mails with a recently introduced Information Retrieval model: the enhanced Topic-based Vector Space Model (eTVSM).  ...  Thereafter, based on this representation, we train several supervised machine-learning algorithms for detecting and filtering junk e-mails.  ... 
doi:10.1016/j.eswa.2011.07.034 fatcat:7bh5676wmzgobphhhasbtzwoqu

Voting for Deceptive Opinion Spam Detection [article]

Tao Wang, Hua Zhu
2014 arXiv   pre-print
Existing approaches mainly focus on developing automatic supervised learning based methods to help users identify deceptive opinion spams.  ...  This work, we used the LSI and Sprinkled LSI technique to reduce the dimension for deception detection.  ...  Existing approaches for spam detection usually focus on developing supervised learning-based algo-rithms to help users identify deceptive opinion spam (Jindal and Liu, 2008; Li et al., 2011; Ott et al  ... 
arXiv:1409.4504v1 fatcat:aejjrx77vnah7pjvhj3azt5y5a

A systematic literature review on spam content detection and classification

Sanaa Kaddoura, Ganesh Chandrasekaran, Daniela Elena Popescu, Jude Hemanth Duraisamy
2022 PeerJ Computer Science  
The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper.  ...  This paper presents a detailed survey on the latest developments in spam text detection and classification in social media.  ...  Only a few studies have used deep learning techniques and semantic approaches to detect spam.  ... 
doi:10.7717/peerj-cs.830 pmid:35174265 pmcid:PMC8802784 fatcat:qv74jetor5eddncvkfpfxshln4

Learning to Detect Deceptive Opinion Spam: A Survey

Yafeng Ren, Donghong Ji
2019 IEEE Access  
In particular, some work based on deep learning has been investigated in last three years for the task.  ...  However, people are frequently deceived by deceptive opinion spam, which is usually used for promoting the products or damaging their reputations because of economic benefit.  ...  DECEPTIVE OPINION SPAM DETECTION Deceptive opinion spam detection is a text-oriented detection task, and aims to classify a review as spam or non-spam by using the review content itself.  ... 
doi:10.1109/access.2019.2908495 fatcat:ni6fut7ruzdw7hjplox456k34y

Semantic Graph Neural Network: A Conversion from Spam Email Classification to Graph Classification

Weisen Pan, Jian Li, Lisa Gao, Liexiang Yue, Yan Yang, Lingli Deng, Chao Deng, Sikandar Ali
2022 Scientific Programming  
The email features are generated from the semantic graph; hence, there is no need of embedding the words into a numerical vector representation.  ...  The performance is better than the state-of-the-art deep learning-based method in terms of spam classification.  ...  Currently, the proposed method is only applicable to text-based email spam detection.  ... 
doi:10.1155/2022/6737080 fatcat:qlljmigz4zad7pc3llenfgbdte

Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

Ala' M. Al-Zoubi, Antonio M. Mora, Hossam Faris
2022 Applied Sciences  
Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem.  ...  The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation.  ...  These aforementioned approaches for both behavior and supervised detection based used widely for detecting spam reviews in literature.  ... 
doi:10.3390/app12073634 fatcat:bz2emxqkkbaavn5u2nbztfhqle

Enhancing representation in the context of multiple-channel spam filtering

María Novo-Lourés, David Ruano-Ordás, Reyes Pavón, Rosalía Laza, Silvana Gómez-Meire, José R. Méndez
2022 Information Processing & Management  
This study addresses the usage of different features to complement synset-based and bag-of-words representations of texts in the context of using classical ML approaches for spam filtering (Ferrara, 2019  ...  for spam filtering.  ...  Additionally, this work was partially endorsed by the project Semantic Knowledge Integration for Content-Based Spam Filtering (TIN2017-84658-C2-1-R) from the Spanish Ministry of Economy, Industry and Competitiveness  ... 
doi:10.1016/j.ipm.2021.102812 fatcat:rzpxn5xybbf4fdbwbvpeofu55i

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

C.L. Lai, K.Q. Xu, Raymond Y.K. Lau, Yuefeng Li, Dawei Song
2010 2010 IEEE International Conference on Data Mining Workshops  
The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework.  ...  Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful  ...  Our work for the detection of untruthful reviews is based on a novel un-supervised classification technique because prominent features for the detection of untruthful reviews may not be available.  ... 
doi:10.1109/icdmw.2010.30 dblp:conf/icdm/LaiXLLS10 fatcat:ua375yxuwzhx3bpzw42omxpqne

An Empirical Study of Online Consumer Review Spam: A Design Science Approach

Raymond Y. K. Lau, Stephen Shaoyi Liao, Kaiquan Xu
2010 International Conference on Information Systems  
Because of the sheer volume of consumer reviews posted to the Internet, a manual approach for the detection and analysis of fake reviews is not practical.  ...  The results of our experiment confirm that the proposed methodology outperforms other well-known baseline methods for detecting untruthful reviews collected from amazon.com.  ...  Review spam detection (Task 6 in Figure 2 ) is carried out based on an unsupervised probabilistic language model (for untruthful review detection), and a supervised classifier (for non-review detection  ... 
dblp:conf/icis/LauLX10 fatcat:3y7uubnlxjdqzepjf5g6wcsx4a

Self-Supervised Losses for One-Class Textual Anomaly Detection [article]

Kimberly T. Mai, Toby Davies, Lewis D. Griffin
2022 arXiv   pre-print
The separability of anomalies and inliers signals that a representation is more effective for detecting semantic anomalies, whilst the presence of narrow feature directions signals a representation that  ...  Overall, the self-supervision approach outperforms other methods under various anomaly detection scenarios, improving the AUROC score on semantic anomalies by 11.6% and on syntactic anomalies by 22.8%  ...  This project made use of time on Tier 2 HPC facility JADE2, funded by EPSRC under grant EP/T022205/1.  ... 
arXiv:2204.05695v1 fatcat:iaqjmm5gqfbbfnhahpx2sh4fmi

A Combined Text-Based and Metadata-Based Deep-Learning Framework for the Detection of Spam Accounts on the Social Media Platform Twitter

Atheer S. Alhassun, Murad A. Rassam
2022 Processes  
Two types of data were used, text-based data with a convolution neural networks (CNN) model and metadata with a simple neural networks model.  ...  This paper addressed the issue of detecting spam accounts in Arabic on Twitter by collecting an Arabic dataset that would be suitable for spam detection.  ...  Acknowledgments: The researchers would like to thank the Deanship of Scientific Research, Qassim University, for funding the publication of this project.  ... 
doi:10.3390/pr10030439 fatcat:vkb53jla5bfsro2cuhse62amwi

A Context-aware Description for Content Filtering on Video Sharing Social Networks

Antonio da Luz, Eduardo Valle, Arnaldo de A. Araujo
2012 2012 IEEE International Conference on Multimedia and Expo  
That is a very challenging task, not only because of the high-level semantic concepts involved, but also because the diverse nature of social networks prevents the use of constrained a priori information  ...  In addition, a spam video is, by nature, context-dependent.  ...  ACKNOWLEDGMENT The authors are thankful to CAPES, CNPq, FAPEMIG and FAPESP, Brazilian funding agencies, for the support to this work.  ... 
doi:10.1109/icme.2012.63 dblp:conf/icmcs/LuzVA12 fatcat:g5h23apwezbylmhtakvdix4uta

Detection of Email Spam using Natural Language Processing Based Random Forest Approach

M.A. Nivedha, S. Raja
2022 International journal of computer science and mobile computing  
spoken by people and the Random Forest approach uses multiple decision trees and uses a random node for filtering the spams.  ...  This can be accomplished through a novel Natural Language Processing based Random Forest (NLP-RF) approach.  ...  At last, logistic regression is used to differentiate genuine users and spammers [16] .The use of text semantic analysis is explored for better spam detection, in which there are 2 semantic levels.  ... 
doi:10.47760/ijcsmc.2022.v11i02.002 fatcat:nqnacdqscfarneroogxr4can3q
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