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Supervised Classification of Social Spammers using a Similarity-based Markov Random Field Approach

Authors:

Nour El-Mawass,

Laurent VercouterAuthors Info & Claims

MISNC '18: Proceedings of the 5th Multidisciplinary International Social Networks Conference

Article No.: 14, Pages 1 - 8

https://doi.org/10.1145/3227696.3227712

Published: 16 July 2018 Publication History

Abstract

Social spam has been plaguing online social networks for years. Being the sites where online users spend most of their time, the battle to capitalize and monetize users' attention is actively fought by both spammers and legitimate sites operators. Social spam detection systems have been proposed as early as 2010. They commonly exploit users' content and behavioral characteristics to build supervised classifiers. Yet spam is an evolving concept, and developed supervised classifiers often become obsolete with the spam community continuously trying to evade detection. In this paper, we use similarity between users to correct evasion-induced errors in the predictions of spam filters. Specifically, we link similar accounts based on their shared applications and build a Markov Random Field model on top of the resulting similarity graph. We use this graphical model in conjunction with traditional supervised classifiers and test the proposed model on a dataset that we recently collected from Twitter. Results show that the proposed model improves the accuracy of classical classifiers by increasing both the precision and the recall of state-of-the-art systems.

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Cited By

Tripathi AGhosh MBharti K(2024)Markov enhanced graph attention network for spammer detection in online social networkKnowledge and Information Systems10.1007/s10115-024-02137-z66:9(5561-5580)Online publication date: 29-May-2024
https://doi.org/10.1007/s10115-024-02137-z
Liu Y(2023)Quantum-based Detection of Higly Semantically Similar Social botFrontiers in Computing and Intelligent Systems10.54097/fcis.v3i3.79913:3(38-42)Online publication date: 4-May-2023
https://doi.org/10.54097/fcis.v3i3.7991
Liu XZhan YJin HWang YZhang Y(2023)Research on the Classification Methods of Social BotsElectronics10.3390/electronics1214303012:14(3030)Online publication date: 10-Jul-2023
https://doi.org/10.3390/electronics12143030
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Index Terms

Supervised Classification of Social Spammers using a Similarity-based Markov Random Field Approach

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MISNC '18: Proceedings of the 5th Multidisciplinary International Social Networks Conference

July 2018

177 pages

ISBN:9781450364652

DOI:10.1145/3227696

Copyright © 2018 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: 16 July 2018

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MISNC '18

MISNC '18: 5th Multidisciplinary International Social Networks Conference

July 16 - 18, 2018

Saint-Etienne, France

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Overall Acceptance Rate 57 of 97 submissions, 59%

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Cited By

Tripathi AGhosh MBharti K(2024)Markov enhanced graph attention network for spammer detection in online social networkKnowledge and Information Systems10.1007/s10115-024-02137-z66:9(5561-5580)Online publication date: 29-May-2024
https://doi.org/10.1007/s10115-024-02137-z
Liu Y(2023)Quantum-based Detection of Higly Semantically Similar Social botFrontiers in Computing and Intelligent Systems10.54097/fcis.v3i3.79913:3(38-42)Online publication date: 4-May-2023
https://doi.org/10.54097/fcis.v3i3.7991
Liu XZhan YJin HWang YZhang Y(2023)Research on the Classification Methods of Social BotsElectronics10.3390/electronics1214303012:14(3030)Online publication date: 10-Jul-2023
https://doi.org/10.3390/electronics12143030
Ellaky ZBenabbou FOuahabi S(2023)Systematic Literature Review of Social Media Bots Detection SystemsJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.04.00435:5(101551)Online publication date: May-2023
https://doi.org/10.1016/j.jksuci.2023.04.004
Ellaky ZBenabbou FOuahabi SSael N(2021)A Survey of Spam Bots Detection in Online Social Networks2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA)10.1109/ICDATA52997.2021.00021(58-65)Online publication date: Jun-2021
https://doi.org/10.1109/ICDATA52997.2021.00021
Al-Dyani WAhmad FKamaruddin S(2021)Binary Bat Algorithm for text feature selection in news events detection model using Markov clusteringCogent Engineering10.1080/23311916.2021.20109239:1Online publication date: 27-Dec-2021
https://doi.org/10.1080/23311916.2021.2010923
Zhao CXin YLi XZhu HYang YChen Y(2020)An Attention-Based Graph Neural Network for Spam Bot Detection in Social NetworksApplied Sciences10.3390/app1022816010:22(8160)Online publication date: 18-Nov-2020
https://doi.org/10.3390/app10228160

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