research-article

Automatically generated spam detection based on sentence-level topic information

Authors:

Yoshihiko Suhara,

Shuichi Nishioka,

Seiji SusakiAuthors Info & Claims

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

Pages 1157 - 1160

https://doi.org/10.1145/2487788.2488140

Published: 13 May 2013 Publication History

Abstract

Spammers use a wide range of content generation techniques with low quality pages known as content spam to achieve their goals. We argue that content spam must be tackled using a wide range of content quality features. In this paper, we propose novel sentence-level diversity features based on the probabilistic topic model. We combine them with other content features to build a content spam classifier. Our experiments show that our method outperforms the conventional methods.

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Cited By

Rani MSumathy S(2022)A Study on Diverse Methods and Performance Measures in Sentiment AnalysisRecent Patents on Engineering10.2174/187221211499920101915495416:3Online publication date: May-2022
https://doi.org/10.2174/1872212114999201019154954
Xu LZhang LLuo W(2022)Pseudo Base Station Spam SMS Identification Based on BiLSTM-Attention2022 11th International Conference on Communications, Circuits and Systems (ICCCAS)10.1109/ICCCAS55266.2022.9825128(216-219)Online publication date: 13-May-2022
https://doi.org/10.1109/ICCCAS55266.2022.9825128
Choi DPark IShin MKim EShin D(2021)Korean Erroneous Sentence Classification With Integrated Eojeol EmbeddingIEEE Access10.1109/ACCESS.2021.30858649(81778-81785)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3085864
Show More Cited By

Index Terms

Automatically generated spam detection based on sentence-level topic information
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

May 2013

1636 pages

ISBN:9781450320382

DOI:10.1145/2487788

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Copyright © 2013 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
CGIBR: Comite Gestor da Internet no Brazil

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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WWW '13

Sponsor:

NICBR
CGIBR

WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

Acceptance Rates

WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Citations

Cited By

Rani MSumathy S(2022)A Study on Diverse Methods and Performance Measures in Sentiment AnalysisRecent Patents on Engineering10.2174/187221211499920101915495416:3Online publication date: May-2022
https://doi.org/10.2174/1872212114999201019154954
Xu LZhang LLuo W(2022)Pseudo Base Station Spam SMS Identification Based on BiLSTM-Attention2022 11th International Conference on Communications, Circuits and Systems (ICCCAS)10.1109/ICCCAS55266.2022.9825128(216-219)Online publication date: 13-May-2022
https://doi.org/10.1109/ICCCAS55266.2022.9825128
Choi DPark IShin MKim EShin D(2021)Korean Erroneous Sentence Classification With Integrated Eojeol EmbeddingIEEE Access10.1109/ACCESS.2021.30858649(81778-81785)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3085864
Asdaghi FSoleimani A(2019)An effective feature selection method for web spam detectionKnowledge-Based Systems10.1016/j.knosys.2018.12.026166(198-206)Online publication date: Feb-2019
https://doi.org/10.1016/j.knosys.2018.12.026
Zhou XOuyang JLi X(2018)Two time-efficient gibbs sampling inference algorithms for biterm topic modelApplied Intelligence10.1007/s10489-017-1004-248:3(730-754)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s10489-017-1004-2
Wei SZhu Y(2017)Cleaning Out Web Spam by Entropy-Based Cascade Outlier DetectionDatabase and Expert Systems Applications10.1007/978-3-319-64471-4_19(232-246)Online publication date: 2-Aug-2017
https://doi.org/10.1007/978-3-319-64471-4_19
Karami AZhou L(2015)Exploiting latent content based features for the detection of static SMS spamsProceedings of the American Society for Information Science and Technology10.1002/meet.2014.1450510115751:1(1-4)Online publication date: 24-Apr-2015
https://doi.org/10.1002/meet.2014.14505101157
Liu HZhang YLin HWu JWu ZZhang X(2013)How Many Zombies Around You?2013 IEEE 13th International Conference on Data Mining10.1109/ICDM.2013.166(1133-1138)Online publication date: Dec-2013
https://doi.org/10.1109/ICDM.2013.166

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