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Survey on Spam Review Detection Using Spam Filtering Algorithms

Dand Shweta B.
2021 International Journal for Research in Applied Science and Engineering Technology  
Sentiment Analysis is a computer study that extracts contextual data from the text. In this study a vast number of online mobile telephone ratings are analyzed.  ...  We classify the text as positive and negative, but we also included feelings of frustration, expectation, disgust, apprehension, happiness, regret, surprise and confidence for spam review detection.  ...  This paper addresses a basic issue of the study of feelings and the classification of feelings of polarity for spam review detection. Data was compiled from online product reviews of Amazon.com.  ... 
doi:10.22214/ijraset.2021.36333 fatcat:vx2nwkts3jdnvlswk5ppnb7n5y

Content based SMS spam filtering

José María Gómez Hidalgo, Guillermo Cajigas Bringas, Enrique Puertas Sánz, Francisco Carrero García
2006 Proceedings of the 2006 ACM symposium on Document engineering - DocEng '06  
Among the wide range of technical measures, Bayesian filters are playing a key role in stopping email spam.  ...  In this paper, we analyze to what extent Bayesian filtering techniques used to block email spam, can be applied to the problem of detecting and stopping mobile spam.  ...  ACKNOWLEDGMENTS We give our thanks to VODAFONE for funding this research and providing us with the Spanish test data. 7.  ... 
doi:10.1145/1166160.1166191 dblp:conf/doceng/HidalgoBSG06 fatcat:obvjrwfdkjbsdh2ff7xvdkbrju

Symbiotic filtering for spam email detection

Clotilde Lopes, Paulo Cortez, Pedro Sousa, Miguel Rocha, Miguel Rio
2011 Expert systems with applications  
This paper presents a novel spam filtering technique called Symbiotic Filtering (SF) that aggregates distinct local filters from several users to improve the overall performance of spam detection.  ...  It allows for the use of social networks to personalize and tailor the set of filters that serve as input to the filtering.  ...  We assume that each user u trains a local filter θ u,t over her/his D u training data.  ... 
doi:10.1016/j.eswa.2011.01.174 fatcat:qrdek3bf7ndupknasdekpqegni

SMS spam filtering: Methods and data

Sarah Jane Delany, Mark Buckley, Derek Greene
2012 Expert systems with applications  
The paper also discusses the issues with data collection and availability for furthering research in this area, analyses a large corpus of SMS spam, and provides some initial benchmark results.  ...  Mobile or SMS spam is a real and growing problem primarily due to the availability of very cheap bulk pre-pay SMS packages and the fact that SMS engenders higher response rates as it is a trusted and personal  ...  spam and non-spam in the training data.  ... 
doi:10.1016/j.eswa.2012.02.053 fatcat:onkgyhoe45h3pi2bthtwlnc7ya

Email Spam Filtering: A Systematic Review

Gordon V. Cormack
2008 Foundations and Trends in Information Retrieval  
The purposes of spam and spam filters are diametrically opposed: spam is effective if it evades filters, while a filter is effective if it recognizes spam.  ...  In doing so we examine the definition of spam, the user's information requirements and the role of the spam filter as one component of a large and complex information universe.  ...  And filter market share will itself influence the design of spam.  ... 
doi:10.1561/1500000006 fatcat:rdmysuohjbd5de54ktguwsoqum

Symbiotic Data Mining for Personalized Spam Filtering

Paulo Cortez, Clotilde Lopes, Pedro Sousa, Miguel Rocha, Miguel Rio
2009 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology  
Unsolicited e-mail (spam) is a severe problem due to intrusion of privacy, online fraud, viruses and time spent reading unwanted messages.  ...  We propose a new CBF-CF hybrid approach called Symbiotic Data Mining (SDM), which aims at aggregating distinct local filters in order to improve filtering at a personalized level using collaboration while  ...  We assume that each user u trains a local filter θ u,t over her/his D u training data.  ... 
doi:10.1109/wi-iat.2009.30 dblp:conf/webi/CortezLSRR09 fatcat:74jpibumfneebc2eime533pb6i

Interactive Spam Filtering with Active Learning and Feature Selection

Masayuki Okabe, Seiji Yamada
2008 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology  
Thus selecting effective and ineffective features is promising approach.Although traditional feature selection methods have been done based on some amount of labeled training data, this assumption does  ...  not hold in interactive spam filtering.  ...  The performance of filtering is influenced by not only the amount of training data, but also the feature set. Feature selection is known to be a good approach [5] .  ... 
doi:10.1109/wiiat.2008.336 dblp:conf/iat/OkabeY08 fatcat:dupmffzkxvcfznpiysabhoixdi

Machine Learning for E-mail Spam Filtering: Review,Techniques and Trends [article]

Alexy Bhowmick, Shyamanta M. Hazarika
2016 arXiv   pre-print
The initial exposition of the background examines the basics of e-mail spam filtering, the evolving nature of spam, spammers playing cat-and-mouse with e-mail service providers (ESPs), and the Machine  ...  We present a comprehensive review of the most effective content-based e-mail spam filtering techniques.  ...  The reason is that when the user trains the spam filter with the poisoned training data, the spam filter 'learns' about such random words as being good evidences of spam [Sanz, 2008] .  ... 
arXiv:1606.01042v1 fatcat:cblnuc4knfhehjwzjeeekbgf3m

An evaluation of Naive Bayes variants in content-based learning for spam filtering

Alexander K. Seewald
2007 Intelligent Data Analysis  
We describe an in-depth analysis of spam-filtering performance of a simple Naive Bayes learner and two current variants.  ...  Below 100 users a locally trained filter may be preferrable. 3 See e.g. http://projects.puremagic.com/greylisting/whitepaper.html 4 Naive Bayes learning with the usual setting for text mining: splitting  ...  Acknowledgements The Austrian Research Institute for Artificial Intelligence is supported by the Austrian Federal Ministry of Education, Science and Culture and by the Austrian Federal Ministry for Transport  ... 
doi:10.3233/ida-2007-11505 fatcat:hqfr7tnfdrbnhe4ixruk3kp3vm

A Voice Spam Filter to Clean Subscribers' Mailbox [chapter]

Seyed Amir Iranmanesh, Hemant Sengar, Haining Wang
2013 Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering  
The uniqueness of our spam filtering approach lies in its independence on the generation of voice spam, regardless whether spammers play same spam content recorded in many different ways, such as human  ...  Using our system, the voice messages left on the media server by callers are matched against a set of spam filtering rules involving the study of call behavioral pattern and the analysis of message content  ...  Matching Process The spam filtering architecture can work in a standalone or distributed collaborative manner.  ... 
doi:10.1007/978-3-642-36883-7_21 fatcat:nclskh6w55ggjh5r3cej52becu

Identifying spam link generators for monitoring emerging web spam

Young-joo Chung, Masashi Toyoda, Masaru Kitsuregawa
2010 Proceedings of the 4th workshop on Information credibility - WICOW '10  
An online learning algorithm is used to handle large scale data, and the effectiveness of various features is examined.  ...  In this paper, we address the question of how we can identify hosts that will generate links to web spam.  ...  This is because we are trying to predict spam link generators when the past data is not available. 3 Our host graph data set can be distributed to researchers for academic and non-commercial use.  ... 
doi:10.1145/1772938.1772950 dblp:conf/www/ChungTK10 fatcat:qe2ppcq7gnekpl2it7daogrkaa

Memetic algorithm for short messaging service spam filter using text normalization and semantic approach

Arnold Adimabua Ojugo, Andrew Okonji Eboka
2020 International Journal of Informatics and Communication Technology (IJ-ICT)  
Spams are unsolicited advertising, adult-themed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter.  ...  Today's popularity of the short messages services (SMS) has created a propitious environment for spamming to thrive.  ...  Bayesian net classifiers are built based on the training data.  ... 
doi:10.11591/ijict.v9i1.pp9-18 fatcat:r4qkuo5dcrf6lflwbajfsiflfe

A social-spam detection framework

De Wang, Danesh Irani, Calton Pu
2011 Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference on - CEAS '11  
amount of data from across social networks; 3) other techniques (such as blacklists and message shingling) can be integrated and centralized; 4) new social networks can plug into the system easily, preventing  ...  There are numerous benefits of the framework including: 1) new spam detected on one social network, can quickly be identified across social networks; 2) accuracy of spam detection will improve with a large  ...  In addition, integrating the detection of spammers' behavior to our framework is considered as future work.  ... 
doi:10.1145/2030376.2030382 dblp:conf/ceas/WangIP11 fatcat:d7m5hsmelbgphc675ft4bmhsre

On Free Speech and Civil Discourse: Filtering Abuse in Blog Comments

D. Sculley
2008 International Conference on Email and Anti-Spam  
In this paper, we investigate the use of user flags to train filters for this task, with the goal of empowering each community to enforce its own standards.  ...  However, such comments may contain abuse, such as personal attacks, offensive remarks about race or religion, or commercial spam, all of which reduce the value of community discussion.  ...  Acknowledgments We gratefully acknowledge Rediff.com for providing the corpus of blog comments and thank our volunteer adjudicators for their painstaking efforts.  ... 
dblp:conf/ceas/Sculley08 fatcat:nmsp6v725rcjhkkbkxukkenjgu

Looking into the past to better classify web spam

Na Dai, Brian D. Davison, Xiaoguang Qi
2009 Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web - AIRWeb '09  
In this paper, we use content features from historical versions of web pages to improve spam classification.  ...  Web spamming techniques aim to achieve undeserved rankings in search results. Research has been widely conducted on identifying such spam and neutralizing its influence.  ...  Acknowledgments This work was supported in part by grants from the National Science Foundation under award IIS-0803605 and IIS-0545875, and an equipment grant from Sun Microsystems.  ... 
doi:10.1145/1531914.1531916 dblp:conf/airweb/DaiDQ09 fatcat:erixqsx6k5eh7bom6tikyim3nq
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