Mining search intents for collaborative cyberporn filtering

NCUIR > College of Electrical Engineering & Computer Science > Department of Electrical Engineering > journal & Dissertation > Item 987654321/107778

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107778

Title:	Mining search intents for collaborative cyberporn filtering
Authors:	李龍豪;Lee, Lung-Hao;Chen, Hsin-Hsi
Contributors:	資訊電機學院電機工程學系
Keywords:	Bibliometrics. Scientometrics;Bibliometrics. Scientometrics. Evaluation;Blacklisting;Blocking;Candidates;Clicks;Collaboration;Content analysis;Data mining;Exact sciences and technology;Experiments;False positive results;Filtering systems;Filtration;Information and communication sciences;Information processing;Information retrieval;Information science. Documentation;Intelligence;Internet;Library and information science. General aspects;Mining;Mining industry;Pornography;Pornography & obscenity;Sciences and techniques of general use;Search engines;Simulation;Studies;Transaction logs;Uniform Resource Locators;URLs;Voting rules;Websites
Date:	2012-02-01
Issue Date:	2026-04-23 14:25:50 (UTC+8)
Publisher:	John Wiley and Sons Inc.;Hoboken, USA: Wiley Subscription Services, Inc., A Wiley Company
Abstract:	摘要： This article presents a search‐intent‐based method to generate pornographic blacklists for collaborative cyberporn filtering. A novel porn‐detection framework that can find newly appearing pornographic web pages by mining search query logs is proposed. First, suspected queries are identified along with their clicked URLs by an automatically constructed lexicon. Then, a candidate URL is determined if the number of clicks satisfies majority voting rules. Finally, a candidate whose URL contains at least one categorical keyword will be included in a blacklist. Several experiments are conducted on an MSN search porn dataset to demonstrate the effectiveness of our method. The resulting blacklist generated by our search‐intent‐based method achieves high precision (0.701) while maintaining a favorably low false‐positive rate (0.086). The experiments of a real‐life filtering simulation reveal that our proposed method with its accumulative update strategy can achieve 44.15% of a macro‐averaging blocking rate, when the update frequency is set to 1 day. In addition, the overblocking rates are less than 9% with time change due to the strong advantages of our search‐intent‐based method. This user‐behavior‐oriented method can be easily applied to search engines for incorporating only implicit collective intelligence from query logs without other efforts. In practice, it is complementary to intelligent content analysis for keeping up with the changing trails of objectionable websites from users' perspectives. 其他題名： J. Am. Soc. Inf. Sci 出版者： Hoboken, USA: Wiley Subscription Services, Inc., A Wiley Company 出版日期： 2012-02 出處： Journal of the American Society for Information Science and Technology, 2012-02, Vol.63 (2), p.366-376 版權： 2011 ASIS&T 版權： 2015 INIST-CNRS 版權： Copyright Wiley Periodicals Inc. Feb 2012 識別號： ISSN: 1532-2882 識別號： ISSN: 2330-1635 識別號： EISSN: 1532-2890 識別號： EISSN: 2330-1643 識別號： DOI: 10.1002/asi.21668
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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