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Spam Detection in Social Networks based on Peer Acceptance

Published: 04 February 2020 Publication History

Abstract

Online Social Networks (OSNs) have become immensely popular for spammers spreading malicious content and links. Using the nature of OSNs, spammers frequently change their behavior to avoid detection. The current approaches for spam detection are mainly based on classification techniques. Classification techniques have known issues such as “data labelling”, “spam drift”, “imbalanced datasets” and “data fabrication” which hinder the performance and the accuracy of spam detection. In this paper we propose a fully unsupervised approach using a user's peer acceptance within the OSN to distinguish spammers from legitimate users. Peer acceptance can be derived based on common shared interests over multiple shared topics. The contribution of this research is an unsupervised method to detect spammers based on users’ peer acceptance generated from users’ post content. While not as accurate as traditional supervised classification techniques, our unsupervised techniques are able to achieve 94.1% accuracy on some datasets without the need for labelling.

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Cited By

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  • (2024)ALBERT4Spam: A Novel Approach for Spam Detection on Social NetworksBilişim Teknolojileri Dergisi10.17671/gazibtd.142623017:2(81-94)Online publication date: 30-Apr-2024
  • (2024)Maximizing Privacy on Social Media through the Safer Tweets Strategy2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10724640(1-9)Online publication date: 24-Jun-2024
  • (2024)Detection and Analysis of Cryptocurrency Scams on TwitterAlgorithmic Aspects in Information and Management10.1007/978-981-97-7801-0_1(3-14)Online publication date: 21-Sep-2024
  • Show More Cited By

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Published In

cover image ACM Other conferences
ACSW '20: Proceedings of the Australasian Computer Science Week Multiconference
February 2020
367 pages
ISBN:9781450376976
DOI:10.1145/3373017
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 February 2020

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Author Tags

  1. Classification
  2. Content Interest
  3. Peer Acceptance
  4. Similarity
  5. Spam Detection

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  • Research-article
  • Research
  • Refereed limited

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ACSW '20
ACSW '20: Australasian Computer Science Week 2020
February 4 - 6, 2020
VIC, Melbourne, Australia

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Overall Acceptance Rate 61 of 141 submissions, 43%

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Cited By

View all
  • (2024)ALBERT4Spam: A Novel Approach for Spam Detection on Social NetworksBilişim Teknolojileri Dergisi10.17671/gazibtd.142623017:2(81-94)Online publication date: 30-Apr-2024
  • (2024)Maximizing Privacy on Social Media through the Safer Tweets Strategy2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10724640(1-9)Online publication date: 24-Jun-2024
  • (2024)Detection and Analysis of Cryptocurrency Scams on TwitterAlgorithmic Aspects in Information and Management10.1007/978-981-97-7801-0_1(3-14)Online publication date: 21-Sep-2024
  • (2023)A Weak-Region Enhanced Bayesian Classification for Spam Content-Based FilteringACM Transactions on Asian and Low-Resource Language Information Processing10.1145/351042022:3(1-18)Online publication date: 2-Apr-2023
  • (2023)SpADe: Multi-Stage Spam Account Detection for Online Social NetworksIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.319883020:4(3128-3143)Online publication date: 1-Jul-2023
  • (2023)A hybrid framework for bot detection on twitter: Fusing digital DNA with BERTMultimedia Tools and Applications10.1007/s11042-023-14730-582:20(30831-30854)Online publication date: 1-Mar-2023
  • (2021)Advances in spam detection for email spam, web spam, social network spam, and review spam: ML-based and nature-inspired-based techniquesJournal of Computer Security10.3233/JCS-210022(1-57)Online publication date: 25-Aug-2021
  • (2021)Detecting malicious activity in Twitter using deep learning techniquesApplied Soft Computing10.1016/j.asoc.2021.107360107(107360)Online publication date: Aug-2021
  • (2021)A Collaborative Abstraction Based Email Spam Filtering with FingerprintsWireless Personal Communications10.1007/s11277-021-09221-5Online publication date: 2-Nov-2021
  • (2021)A Drift Aware Hierarchical Test Based Approach for Combating Social Spammers in Online Social NetworksData Mining10.1007/978-981-16-8531-6_4(47-61)Online publication date: 9-Dec-2021

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