A comparative study of machine learning techniques in blog comments spam filtering

Romero, Christopher J.; García-Valdéz, Mario; Alanís, Alma Y.

doi:10.1109/ijcnn.2010.5596677

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…Accuracy (%) F-measure The best results available in the literature KL-divergence [2] 83.00 -SMO [3] 84.61 -C4.5 [4] 86.00 0.890 C4.5 auto-supervised [4] 71.58 0.825 The best results presented in this paper SVM-P (metadata) -balanced 97. The results indicated that, in general, the evaluated techniques achieved good performance, regardless of the used attribute.…”

Section: Classifiersmentioning

confidence: 90%

“…It aims to verify the presence of links that may take to undesired pages. The authors also created the first public blog spam database that has been used in later studies [1], [3], [4].…”

Section: Related Workmentioning

confidence: 99%

“…Romero et al [3] performed a comparative study of four machine learning techniques in blog comment spam filtering, by extracting attributes of the message content. Among the evaluated classifiers: Naïve Bayes, k-nearest neighbors, artificial neural networks and support vector machines (SMO), the latter achieved the best result.…”

Section: Related Workmentioning

confidence: 99%

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Learning to Block Undesired Comments in the Blogosphere

Almeida

Alberto

2013

2013 12th International Conference on Machine Learning and Applications

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A significant amount of the available information on web sites come from the interaction with users, such as news sites and blogs, where readers can post comments and sometimes develop habits of frequenting them. Some blogs specialize in certain subjects, receive the users credibility and become references in the field. However, the ease of inserting content through text comments makes room for unwanted messages, which affect the user experience, reduce the quality of the information provided by the websites and indirectly causing personal and economic losses. Given this scenario, this paper presents a comprehensive analysis of machine learning techniques applied to automatically detect undesired comments posted on blogs. Experiments carried out with a real and public database indicate that support vector machines and logistic regression, trained with both attributes extracted from the text messages and metadata from the posts, are promising in the task of filtering unwanted comments.

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Section: Classifiersmentioning

confidence: 90%

“…It aims to verify the presence of links that may take to undesired pages. The authors also created the first public blog spam database that has been used in later studies [1], [3], [4].…”

Section: Related Workmentioning

confidence: 99%

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Learning to Block Undesired Comments in the Blogosphere

Almeida

Alberto

2013

2013 12th International Conference on Machine Learning and Applications

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“…More time might be needed as in the worst case, all data points might take point in decision. [13]. c) DT: Decision tree is a simple structure where non-leaf nodes represent the conditional tests of attributes or features and leaf nodes contain the class label in which each data predicted into.Tree-shaped structures that rep-resent sets of decisions.…”

Section: A Various Algorithms For Filtering Text Spam 1) Emails A) Svmmentioning

confidence: 99%

Image and Text Spam Mail Filtering System

S¹

2017

IJRASET

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Spam mail is not only a waste of time for the recipients but can also spread harmful messages and viruses worms. It is also harmful for machine, it is not secure. In transmitting information, text is always used in image spam. Spam mail takes lot of space of inbox; it is time consuming and irritating process to manually filter spam and legitimate mail. Therefore in this study, I classify mail images by the configuration of letters text and images. In this system I have proposed a spam detection method that uses sober operators for edge detection and a multiple filter using Sobel operators and AOCR, This is beneficial for identifying spam mail. With the wide application of E-mail for communication, unwanted email means spam mail has become a major problem for E-mail users. This is also major problem for internet users.

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“…As spam has different types, various methods have been proposed for detecting and filtering them. Authors in performed a study on machine learning techniques for spam filtering in blog comments. Thus, they compared four famous machine learning techniques: Naïve Bayes, K‐nearest neighbor, neural networks, and Support Vector Machines.…”

Section: Alternatives To Captchasmentioning

confidence: 99%

CAPTCHA and its Alternatives: A Review

Moradi

Keyvanpour

2014

Security Comm Networks

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Nowadays, because of the undeniable impact of the Internet on all aspects of human life, security preserving has received more attention. To reach an acceptable level of security, Completely Automatic Public Turing test to tell Computer and Human Apart or simply CAPTCHA as a security preserving tool has been tailored for situations that need to prevent bots from doing a specific action; for example, signing up and downloading. Simultaneously, it should be also designed in such a way to allow humans to perform the same action. Despite its advantageous applications, there are several important issues such as security, usability, and accessibility that make its use controversial. In this paper, attempts were made to do a comprehensive review on various aspects and state-of-the-art of CAPTCHA in general and its alternatives in particular to help researchers easily focus on specific issues for the sake of proposing new solutions and ideas. Regarding the advancement in CAPTCHA development, new classifications were proposed to categorize different variations of CAPTCHAs and their problems and then compare them. Moreover, different types of CAPTCHAs' alternatives were classified and evaluated by introducing several proposed measures. This evaluation could come in handy for future studies that aim to develop new techniques for overcoming current deficiencies.

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A comparative study of machine learning techniques in blog comments spam filtering

Cited by 12 publications

References 8 publications

Learning to Block Undesired Comments in the Blogosphere

Learning to Block Undesired Comments in the Blogosphere

Image and Text Spam Mail Filtering System

CAPTCHA and its Alternatives: A Review

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