research-article

Linked latent Dirichlet allocation in web spam filtering

Authors:

István Bíró,

Dávid Siklósi,

Jácint Szabó,

András A. BenczúrAuthors Info & Claims

AIRWeb '09: Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web

Pages 37 - 40

https://doi.org/10.1145/1531914.1531922

Published: 21 April 2009 Publication History

Abstract

Latent Dirichlet allocation (LDA) (Blei, Ng, Jordan 2003) is a fully generative statistical language model on the content and topics of a corpus of documents. In this paper we apply an extension of LDA for web spam classification. Our linked LDA technique takes also linkage into account: topics are propagated along links in such a way that the linked document directly influences the words in the linking document. The inferred LDA model can be applied for classification as dimensionality reduction similarly to latent semantic indexing. We test linked LDA on the WEBSPAM-UK2007 corpus. By using BayesNet classifier, in terms of the AUC of classification, we achieve 3% improvement over plain LDA with BayesNet, and 8% over the public link features with C4.5. The addition of this method to a log-odds based combination of strong link and content baseline classifiers results in a 3% improvement in AUC. Our method even slightly improves over the best Web Spam Challenge 2008 result.

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https://doi.org/10.1109/ASSIC60049.2024.10507910
Li MZhao LSrinivas S(2023)It is about inclusion! Mining online reviews to understand the needs of adaptive clothing customersInternational Journal of Consumer Studies10.1111/ijcs.1289547:3(1157-1172)Online publication date: 18-Jan-2023
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Khosa SRustam FMehmood AChoi GAshraf I(2023)Incorporating Word Embedding and Hybrid Model Random Forest Softmax Regression for Predicting News CategoriesMultimedia Tools and Applications10.1007/s11042-023-16491-7Online publication date: 15-Sep-2023
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Index Terms

Linked latent Dirichlet allocation in web spam filtering
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
  2. Information storage systems

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cover image ACM Other conferences

AIRWeb '09: Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web

April 2009

67 pages

ISBN:9781605584386

DOI:10.1145/1531914

Editors:
Dennis Fetterly
Microsoft Research
,
Zoltán Gyöngyi
Google Research

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 April 2009

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AIRWeb '09: AIRWeb '09, 5th International Workshop on Adversarial Information Retrieval on the Web

April 21, 2009

Madrid, Spain

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Shivanadhuni SPrakash R(2024)A Review: Comprehensive study on societal Analysis for health care system Using topic modeling Paradigms2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)10.1109/ASSIC60049.2024.10507910(1-5)Online publication date: 27-Jan-2024
https://doi.org/10.1109/ASSIC60049.2024.10507910
Li MZhao LSrinivas S(2023)It is about inclusion! Mining online reviews to understand the needs of adaptive clothing customersInternational Journal of Consumer Studies10.1111/ijcs.1289547:3(1157-1172)Online publication date: 18-Jan-2023
https://doi.org/10.1111/ijcs.12895
Khosa SRustam FMehmood AChoi GAshraf I(2023)Incorporating Word Embedding and Hybrid Model Random Forest Softmax Regression for Predicting News CategoriesMultimedia Tools and Applications10.1007/s11042-023-16491-7Online publication date: 15-Sep-2023
https://doi.org/10.1007/s11042-023-16491-7
Zosimov VBulgakova O(2023)Enhancing Readability in Custom Templates for Displaying Semantically Marked InformationAdvances in Computer Science for Engineering and Education VI10.1007/978-3-031-36118-0_13(137-146)Online publication date: 19-Aug-2023
https://doi.org/10.1007/978-3-031-36118-0_13
Hinek M(2021)Fesztivállátogatók véleményeinek számítógéppel támogatott tematikus modellezése– egy kísérlet eredményei = Computer-aided topic modelling based on festival-goers’ opinions– results of an experimentTurizmus Bulletin10.14267/TURBULL.2021v21n1.121:1(4-12)Online publication date: 21-Apr-2021
https://doi.org/10.14267/TURBULL.2021v21n1.1
Liu JSu YLv SHuang C(2020)Detecting Web Spam Based on Novel Features from Web Page Source CodeSecurity and Communication Networks10.1155/2020/66621662020Online publication date: 17-Dec-2020
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Kermanidis K(2019)Identifying Latent Semantics in Action Games for Player ModelingInternational Journal of Gaming and Computer-Mediated Simulations10.4018/IJGCMS.201904010111:2(1-21)Online publication date: Apr-2019
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Luckner MKozik R(2019)Practical Web Spam Lifelong Machine Learning System with Automatic Adjustment to Current Lifecycle PhaseSecurity and Communication Networks10.1155/2019/65870202019Online publication date: 20-Feb-2019
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Singh ABhushan SVij S(2019)Filtering spam messages and mails using fuzzy C means algorithm2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU)10.1109/IoT-SIU.2019.8777483(1-5)Online publication date: Apr-2019
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Ayele WJuell-Skielse G(2018)Unveiling Topics from Scientific Literature on the Subject of Self-driving Cars using Latent Dirichlet Allocation2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)10.1109/IEMCON.2018.8615056(1113-1119)Online publication date: Nov-2018
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