research-article

Analyzing and Detecting Collusive Users Involved in Blackmarket Retweeting Activities

Authors:

Hridoy Sankar Dutta,

Tanmoy ChakrabortyAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 11, Issue 3

Article No.: 35, Pages 1 - 24

https://doi.org/10.1145/3380537

Published: 18 April 2020 Publication History

Abstract

With the rise in popularity of social media platforms like Twitter, having higher influence on these platforms has a greater value attached to it, since it has the power to influence many decisions in the form of brand promotions and shaping opinions. However, blackmarket services that allow users to inorganically gain influence are a threat to the credibility of these social networking platforms. Twitter users can gain inorganic appraisals in the form of likes, retweets, and follows through these blackmarket services either by paying for them or by joining syndicates wherein they gain such appraisals by providing similar appraisals to other users. These customers tend to exhibit a mix of organic and inorganic retweeting behavior, making it tougher to detect them.

In this article, we investigate these blackmarket customers engaged in collusive retweeting activities. We collect and annotate a novel dataset containing various types of information about blackmarket customers and use these sources of information to construct multiple user representations. We adopt Weighted Generalized Canonical Correlation Analysis (WGCCA) to combine these individual representations to derive user embeddings that allow us to effectively classify users as: genuine users, bots, promotional customers, and normal customers. Our method significantly outperforms state-of-the-art approaches (32.95% better macro F1-score than the best baseline).

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Index Terms

Analyzing and Detecting Collusive Users Involved in Blackmarket Retweeting Activities
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks
2. Security and privacy

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 11, Issue 3

Survey Paper and Regular Papers

June 2020

286 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3392081

Editor:
Yu Zheng
JD Finance, China

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Copyright © 2020 ACM.

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Publication History

Published: 18 April 2020

Accepted: 01 January 2020

Revised: 01 January 2020

Received: 01 July 2019

Published in TIST Volume 11, Issue 3

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SPARC
Infosys Centre of AI, IIIT-Delhi, India
Ramanujan Fellowship, DST
Google India Faculty Award

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https://doi.org/10.1177/10567879241252398
Xu ZLi LLi HSun QHu SJi RChua TLauw HSi LTerzi ETsaparas P(2023)Self-supervised Graph Representation Learning for Black Market Account DetectionProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570466(330-338)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570466
Xiao JZeng JMiao XCao YZhao JFeng Z(2023)USDSE: A Novel Method to Improve Service Reputation Based on Double-Side EvaluationMachine Learning for Cyber Security10.1007/978-3-031-20102-8_37(484-498)Online publication date: 13-Jan-2023
https://doi.org/10.1007/978-3-031-20102-8_37
Mazza MCola GTesconi M(2022)Ready-to-(ab)use: From fake account trafficking to coordinated inauthentic behavior on TwitterOnline Social Networks and Media10.1016/j.osnem.2022.10022431(100224)Online publication date: Sep-2022
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Gera SSinha A(2022)C-ANN: a deep leaning model for detecting black-marketed colluders in Twitter social networkNeural Computing and Applications10.1007/s00521-021-06756-334:18(15113-15127)Online publication date: 1-Sep-2022
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Alkhamees MAlsaleem SAl-Qurishi MAl-Rubaian MHussain A(2021)User trustworthiness in online social networks: A systematic reviewApplied Soft Computing10.1016/j.asoc.2021.107159(107159)Online publication date: Feb-2021
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