WebGuard: Web based adult content detection and filtering system

Hammami, Mohamed; Chahir, Youssef; Chen, L.

doi:10.1109/wi.2003.1241271

Cited by 35 publications

(10 citation statements)

References 2 publications

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“…Traditionally, building a database of blacklist websites is the widespread method [1]. The websites containing pornographic 10 images are added into the blacklist to block user access.…”

Section: Introductionmentioning

confidence: 99%

Pornographic image detection utilizing deep convolutional neural networks

Nian

Wang

et al. 2016

Neurocomputing

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“…Traditionally, building a database of blacklist websites is the widespread method [1]. The websites containing pornographic 10 images are added into the blacklist to block user access.…”

Section: Introductionmentioning

confidence: 99%

Pornographic image detection utilizing deep convolutional neural networks

Nian

Wang

et al. 2016

Neurocomputing

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“…It extract text, images, URL names and analyse them with data mining technique. [10] V. WEB FILTER TOOLS There are number of solutions are available for schools/colleges/institutions their web filtering needs. 1.…”

Section: Research Work Conducted On Web Content Filteringmentioning

confidence: 99%

Web Content Filtration Using Different Web Mining Techniques in Educational System: An Overview

Modi¹,

Jagtap²

2017

IJARCSSE

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Abstract-Internet is widely used as ICT tool in today's education. The web content filtering is essential tool used to filter offensive, unwanted web content from web pages. Internet is widely used in educational organisations as an ICT tool. To protect our tech savvy student from accessing adult sites, offensive, unwanted data in absence of teachers and parents in school as well as home. A strong filter is required to prevent this type of unsolicited activity. In this paper we review the existing filter tools and their techniques.

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“…The linguistic formality weights different types of words, with nouns, adjectives, articles and prepositions as positive elements, and adverbs, verbs and interjections as negative. The in-house classifier is based on linguistic features designed on top of the SpaCy NLP toolkit, 11 such as text readability, n-gram counts, constituency, part-of-speech, lexical features, casing and punctuation, entity, subjectivity (TextBlob NLP) 12 and Word2Vec 13 features. We also include low-level features, such as the frequency of punctuation, numbers, "5W1H" words, superlative adjectives and adverbs.…”

Section: Trustworthinessmentioning

confidence: 99%

“…sports, entertainment) according to a topic taxonomy (only top-level categories) [21]. We also add to this group the adult score as extracted from the CAP, that suggests whether the product advertised is adult-related such as dating websites [12].…”

Section: Product/servicementioning

confidence: 99%

Predicting Pre-click Quality for Native Advertisements

Zhou

Реди

Haines

et al. 2016

Proceedings of the 25th International Conference on World Wide Web

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Native advertising is a specific form of online advertising where ads replicate the look-and-feel of their serving platform. In such context, providing a good user experience with the served ads is crucial to ensure long-term user engagement. In this work, we explore the notion of ad quality, namely the effectiveness of advertising from a user experience perspective. We design a learning framework to predict the pre-click quality of native ads. More specifically, we look at detecting offensive native ads, showing that, to quantify ad quality, ad offensive user feedback rates are more reliable than the commonly used click-through rate metrics. We then conduct a crowd-sourcing study to identify which criteria drive user preferences in native advertising. We translate these criteria into a set of ad quality features that we extract from the ad text, image and advertiser, and then use them to train a model able to identify offensive ads. We show that our model is very effective in detecting offensive ads, and provide in-depth insights on how different features affect ad quality. Finally, we deploy a preliminary version of such model and show its effectiveness in the reduction of the offensive ad feedback rate. 1 We use advertisement and ad interchangeably. Copyright is held by the International World Wide Web Conference Committee (IW3C2). IW3C2 reserves the right to provide a hyperlink to the author's site if the Material is used in electronic media.

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WebGuard: Web based adult content detection and filtering system

Cited by 35 publications

References 2 publications

Pornographic image detection utilizing deep convolutional neural networks

Pornographic image detection utilizing deep convolutional neural networks

Web Content Filtration Using Different Web Mining Techniques in Educational System: An Overview

Predicting Pre-click Quality for Native Advertisements

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