research-article

Leveraging knowledge across media for spammer detection in microblogging

Authors:

Huan LiuAuthors Info & Claims

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Pages 547 - 556

https://doi.org/10.1145/2600428.2609632

Published: 03 July 2014 Publication History

Abstract

While microblogging has emerged as an important information sharing and communication platform, it has also become a convenient venue for spammers to overwhelm other users with unwanted content. Currently, spammer detection in microblogging focuses on using social networking information, but little on content analysis due to the distinct nature of microblogging messages. First, label information is hard to obtain. Second, the texts in microblogging are short and noisy. As we know, spammer detection has been extensively studied for years in various media, e.g., emails, SMS and the web. Motivated by abundant resources available in the other media, we investigate whether we can take advantage of the existing resources for spammer detection in microblogging. While people accept that texts in microblogging are different from those in other media, there is no quantitative analysis to show how different they are. In this paper, we first perform a comprehensive linguistic study to compare spam across different media. Inspired by the findings, we present an optimization formulation that enables the design of spammer detection in microblogging using knowledge from external media. We conduct experiments on real-world Twitter datasets to verify (1) whether email, SMS and web spam resources help and (2) how different media help for spammer detection in microblogging.

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Cited By

Chen TLiu JWu YTian YTong ENiu WLi YXiang YWang W(2021)Survey on Astroturfing Detection and Analysis from an Information Technology PerspectiveSecurity and Communication Networks10.1155/2021/32946102021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/3294610
Li SJiang FQin YZheng K(2021)Social Spammer Detection Based on PSO-CatBoostSecurity, Privacy, and Anonymity in Computation, Communication, and Storage10.1007/978-3-030-68851-6_28(382-395)Online publication date: 5-Feb-2021
https://doi.org/10.1007/978-3-030-68851-6_28
Dhaka DMehrotra M(2019)Cross-Domain Spam Detection in Social Media: A SurveyEmerging Technologies in Computer Engineering: Microservices in Big Data Analytics10.1007/978-981-13-8300-7_9(98-112)Online publication date: 18-May-2019
https://doi.org/10.1007/978-981-13-8300-7_9
Show More Cited By

Index Terms

Leveraging knowledge across media for spammer detection in microblogging
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Conferences

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

1330 pages

ISBN:9781450322577

DOI:10.1145/2600428

General Chairs:
Shlomo Geva
Queensland University of Technology
,
Andrew Trotman
University of Dunedin
,
Program Chairs:
Peter Bruza
Queensland University of Technology
,
Charles L.A. Clarke
University of Waterloo
,
Kal Järvelin
University of Tampere

Copyright © 2014 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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

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Published: 03 July 2014

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SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 6 - 11, 2014

Queensland, Gold Coast, Australia

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SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Chen TLiu JWu YTian YTong ENiu WLi YXiang YWang W(2021)Survey on Astroturfing Detection and Analysis from an Information Technology PerspectiveSecurity and Communication Networks10.1155/2021/32946102021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/3294610
Li SJiang FQin YZheng K(2021)Social Spammer Detection Based on PSO-CatBoostSecurity, Privacy, and Anonymity in Computation, Communication, and Storage10.1007/978-3-030-68851-6_28(382-395)Online publication date: 5-Feb-2021
https://doi.org/10.1007/978-3-030-68851-6_28
Dhaka DMehrotra M(2019)Cross-Domain Spam Detection in Social Media: A SurveyEmerging Technologies in Computer Engineering: Microservices in Big Data Analytics10.1007/978-981-13-8300-7_9(98-112)Online publication date: 18-May-2019
https://doi.org/10.1007/978-981-13-8300-7_9
Li LLiu JSun YXu GYuan JZhong L(2018)Unsupervised keyword extraction from microblog posts via hashtagsJournal of Web Engineering10.5555/3370048.337005317:1-2(93-120)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.5555/3370048.3370053
Wu FWu CLiu JCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Semi-Supervised Collaborative Learning for Social Spammer and Spam Message Detection in MicrobloggingProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3269324(1791-1794)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3269324
Li YKim DZhang JDoran D(2018)Detecting Suspicious Members in an Online Emotional Support ServiceSecurity and Privacy in Communication Networks10.1007/978-3-030-01704-0_2(22-42)Online publication date: 29-Dec-2018
https://doi.org/10.1007/978-3-030-01704-0_2
Liang BXu BYang DLiu QXiao Y(2018)Social Communication Network: Case StudyEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_289(2576-2583)Online publication date: 12-Jun-2018
https://doi.org/10.1007/978-1-4939-7131-2_289
Fu HXie XRui YGong NSun GChen E(2017)Robust Spammer Detection in MicroblogsACM Transactions on Intelligent Systems and Technology10.1145/30866378:6(1-31)Online publication date: 18-Aug-2017
https://dl.acm.org/doi/10.1145/3086637
Zhou YKim DZhang JLiu LJin HJin HLiu T(2017)ProGuard: Detecting Malicious Accounts in Social-Network-Based Online PromotionsIEEE Access10.1109/ACCESS.2017.26542725(1990-1999)Online publication date: 2017
https://doi.org/10.1109/ACCESS.2017.2654272
Shen HMa FZhang XZong LLiu XLiang W(2017)Discovering social spammers from multiple viewsNeurocomputing10.1016/j.neucom.2016.11.013225:C(49-57)Online publication date: 15-Feb-2017
https://dl.acm.org/doi/10.1016/j.neucom.2016.11.013
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