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research-article

Twitter spam detection based on deep learning

Authors:

Yang XiangAuthors Info & Claims

ACSW '17: Proceedings of the Australasian Computer Science Week Multiconference

Article No.: 3, Pages 1 - 8

https://doi.org/10.1145/3014812.3014815

Published: 31 January 2017 Publication History

Abstract

Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning-based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 80%. However, due to the problems of spam drift and information fabrication, these machine-learning based methods cannot efficiently detect spam activities in real-life scenarios. Moreover, the blacklisting method cannot catch up with the variations of spamming activities as manually inspecting suspicious URLs is extremely time-consuming. In this paper, we proposed a novel technique based on deep learning techniques to address the above challenges. The syntax of each tweet will be learned through WordVector Training Mode. We then constructed a binary classifier based on the preceding representation dataset. In experiments, we collected and implemented a 10-day real Tweet datasets in order to evaluate our proposed method. We first studied the performance of different classifiers, and then compared our method to other existing text-based methods. We found that our method largely outperformed existing methods. We further compared our method to non-text-based detection techniques. According to the experiment results, our proposed method was more accurate.

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Cited By

Sindiramutty STan CLau SThangaveloo RGharib AManchuri AKhan NTee WMuniandy L(2024)Explainable AI for CybersecurityAdvances in Explainable AI Applications for Smart Cities10.4018/978-1-6684-6361-1.ch002(31-97)Online publication date: 18-Jan-2024
https://doi.org/10.4018/978-1-6684-6361-1.ch002
Erbay HBakır RBakır H(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
https://doi.org/10.17671/gazibtd.1426230
Gao HZhang HWang JZhang XWang HLi WTu T(2024)NUAT-GAN: Generating Black-Box Natural Universal Adversarial Triggers for Text Classifiers Using Generative Adversarial NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.341684919(6484-6498)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3416849
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Index Terms

Twitter spam detection based on deep learning
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Social engineering attacks
      1. Phishing
      2. Spoofing attacks

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Published In

cover image ACM Other conferences

ACSW '17: Proceedings of the Australasian Computer Science Week Multiconference

January 2017

615 pages

ISBN:9781450347686

DOI:10.1145/3014812

Copyright © 2017 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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Publication History

Published: 31 January 2017

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ACSW 2017

ACSW 2017: Australasian Computer Science Week 2017

January 30 - February 3, 2017

Geelong, Australia

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ACSW '17 Paper Acceptance Rate 78 of 156 submissions, 50%;

Overall Acceptance Rate 204 of 424 submissions, 48%

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Cited By

Sindiramutty STan CLau SThangaveloo RGharib AManchuri AKhan NTee WMuniandy L(2024)Explainable AI for CybersecurityAdvances in Explainable AI Applications for Smart Cities10.4018/978-1-6684-6361-1.ch002(31-97)Online publication date: 18-Jan-2024
https://doi.org/10.4018/978-1-6684-6361-1.ch002
Erbay HBakır RBakır H(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
https://doi.org/10.17671/gazibtd.1426230
Gao HZhang HWang JZhang XWang HLi WTu T(2024)NUAT-GAN: Generating Black-Box Natural Universal Adversarial Triggers for Text Classifiers Using Generative Adversarial NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.341684919(6484-6498)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3416849
Tusher EIsmail MRahman MAlenezi AUddin M(2024)Email Spam: A Comprehensive Review of Optimize Detection Methods, Challenges, and Open Research ProblemsIEEE Access10.1109/ACCESS.2024.346799612(143627-143657)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3467996
Rashidi ASalehi MNajari S(2024)CGANS: a code-based GAN for spam detection in social mediaSocial Network Analysis and Mining10.1007/s13278-024-01379-714:1Online publication date: 17-Nov-2024
https://doi.org/10.1007/s13278-024-01379-7
Alves VRibeiro J(2024)Detection and Classification of Spam in Social Media Comments Using Artificial Intelligence – A Case StudyProgress in Artificial Intelligence10.1007/978-3-031-73500-4_26(311-323)Online publication date: 16-Nov-2024
https://doi.org/10.1007/978-3-031-73500-4_26
Jamal SWimmer HSarker I(2024)An improved transformer‐based model for detecting phishing, spam and ham emails: A large language model approachSECURITY AND PRIVACY10.1002/spy2.402Online publication date: 24-Apr-2024
https://doi.org/10.1002/spy2.402
Dar MIqbal FLatif RAltaf AJamail N(2023)Policy-Based Spam Detection of Tweets DatasetElectronics10.3390/electronics1212266212:12(2662)Online publication date: 14-Jun-2023
https://doi.org/10.3390/electronics12122662
Liu HHuang XZhang XLi QMa FWang WChen HYu HZhang XElkind E(2023)Boosting decision-based black-box adversarial attack with gradient priorsProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/133(1195-1203)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/133
ÇITLAK ODÖRTERLER MDOGRU İ(2023)A Hybrid Spam Detection Framework for Social NetworksSosyal Ağlar için Hibrit Bir Spam Algılama FrameworkPoliteknik Dergisi10.2339/politeknik.93378526:2(823-837)Online publication date: 5-Jul-2023
https://doi.org/10.2339/politeknik.933785
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