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Fighting against web spam: a novel propagation method based on click-through data

Published: 12 August 2012 Publication History

Abstract

Combating Web spam is one of the greatest challenges for Web search engines. State-of-the-art anti-spam techniques focus mainly on detecting varieties of spam strategies, such as content spamming and link-based spamming. Although these anti-spam approaches have had much success, they encounter problems when fighting against a continuous barrage of new types of spamming techniques. We attempt to solve the problem from a new perspective, by noticing that queries that are more likely to lead to spam pages/sites have the following characteristics: 1) they are popular or reflect heavy demands for search engine users and 2) there are usually few key resources or authoritative results for them. From these observations, we propose a novel method that is based on click-through data analysis by propagating the spamicity score iteratively between queries and URLs from a few seed pages/sites. Once we obtain the seed pages/sites, we use the link structure of the click-through bipartite graph to discover other pages/sites that are likely to be spam. Experiments show that our algorithm is both efficient and effective in detecting Web spam. Moreover, combining our method with some popular anti-spam techniques such as TrustRank achieves improvement compared with each technique taken individually.

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    cover image ACM Conferences
    SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
    August 2012
    1236 pages
    ISBN:9781450314725
    DOI:10.1145/2348283
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    Published: 12 August 2012

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    Author Tags

    1. click-through data/bipartite graph
    2. label propagation
    3. semi-supervised algorithm
    4. spam detection
    5. web search engine

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    • (2021)Exploiting the Community Structure of Fraudulent Keywords for Fraud Detection in Web SearchJournal of Computer Science and Technology10.1007/s11390-021-0218-236:5(1167-1183)Online publication date: 30-Sep-2021
    • (2020)Recommending Inferior Results: A General and Feature-Free Model for Spam DetectionProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411900(955-974)Online publication date: 19-Oct-2020
    • (2020)Omni-Channel Strategy in the Framework of the Search EnginesManaging Customer Experiences in an Omnichannel World: Melody of Online and Offline Environments in the Customer Journey10.1108/978-1-80043-388-520201017(211-232)Online publication date: 26-Nov-2020
    • (2018)Identifying Price Sensitive Customers in E-commerce Platforms for Recommender SystemsInformation Retrieval10.1007/978-3-030-01012-6_18(225-236)Online publication date: 19-Sep-2018
    • (2017)Measuring and Visualizing the Scrappiness Level of a Website2017 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)10.1109/SYNASC.2017.00057(304-311)Online publication date: Sep-2017
    • (2017)A Hybrid Abnormal Advertising Traffic Detection Method2017 IEEE International Conference on Big Knowledge (ICBK)10.1109/ICBK.2017.50(236-241)Online publication date: Aug-2017
    • (2017)Incorporating Position Bias into Click-Through Bipartite GraphInformation Retrieval10.1007/978-3-319-68699-8_5(57-68)Online publication date: 21-Oct-2017
    • (2016)Resisting tag spam by leveraging implicit user behaviorsProceedings of the VLDB Endowment10.14778/3021924.302193910:3(241-252)Online publication date: 1-Nov-2016
    • (2015)Learning Entity Types from Query Logs via Graph-Based ModelingProceedings of the 24th ACM International on Conference on Information and Knowledge Management10.1145/2806416.2806498(603-612)Online publication date: 17-Oct-2015
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