Automated twitter author clustering with unsupervised learning for social media forensics

Sicong Shao, Cihan Tunc, Amany Al-Shawi, Salim Hariri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Twitter is one of the key social media platforms, which is also used for cyber-crimes. Hence, monitoring and detecting the malicious activities of Twitter users is critically important for cybersecurity concerns around the globe since cybercriminals are heavily using Twitter for illegal purpose. It is increasingly common for cybercriminals signing up many accounts while masquerading different users for malicious behaviors. This fact has brought forward the issue of identifying the authors of Twitter accounts. In this paper, we propose a novel approach through a combination of feature extraction methods and then convert high dimensional data to kernel matrix for Twitter author clustering. The experimental results show that our approach can be used to effectively identify the groups among more than one hundred Twitter aliases even without knowing the number of authors.

Original languageEnglish (US)
Title of host publication16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728150529
DOIs
StatePublished - Nov 2019
Event16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019 - Abu Dhabi, United Arab Emirates
Duration: Nov 3 2019Nov 7 2019

Publication series

NameProceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
Volume2019-November
ISSN (Print)2161-5322
ISSN (Electronic)2161-5330

Conference

Conference16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019
CountryUnited Arab Emirates
CityAbu Dhabi
Period11/3/1911/7/19

Keywords

  • Author clustering
  • Author identification
  • Cybersecurity
  • Social media
  • Twitter
  • Unsupervised learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Shao, S., Tunc, C., Al-Shawi, A., & Hariri, S. (2019). Automated twitter author clustering with unsupervised learning for social media forensics. In 16th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2019 [9035286] (Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA; Vol. 2019-November). IEEE Computer Society. https://doi.org/10.1109/AICCSA47632.2019.9035286