An End-to-End Framework to Identify Pathogenic Social Media Accounts on Twitter

Elham Shaabani, Ashkan Sadeghi Mobarakeh, Hamidreza Alvari, Paulo Shakarian

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

1 Scopus citations

Abstract

Pathogenic Social Media (PSM) accounts such as terrorist supporter accounts and fake news writers have the capability of spreading disinformation to viral proportions. Early detection of PSM accounts is crucial as they are likely to be key users to make malicious information 'viral'. In this paper, we adopt the causal inference framework along with graph-based metrics in order to distinguish PSMs from normal users within a short time of their activities. We propose both supervised and semi-supervised approaches without taking the network information and content into account. Results on a real-world the dataset from Twitter accentuates the advantage of our proposed frameworks. We show our approach achieves 0.28 improvement in F1 score over existing approaches with the precision of 0.90 and F1 score of 0.63.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 2nd International Conference on Data Intelligence and Security, ICDIS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages128-135
Number of pages8
ISBN (Electronic)9781728120805
DOIs
StatePublished - Jun 2019
Event2nd International Conference on Data Intelligence and Security, ICDIS 2019 - South Padre Island, United States
Duration: Jun 28 2019Jun 30 2019

Publication series

NameProceedings - 2019 2nd International Conference on Data Intelligence and Security, ICDIS 2019

Conference

Conference2nd International Conference on Data Intelligence and Security, ICDIS 2019
CountryUnited States
CitySouth Padre Island
Period6/28/196/30/19

Keywords

  • bipartite graph analysis
  • causal reasoning
  • deep learning approach
  • Fake account detection
  • Pathogenic account

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

Cite this

Shaabani, E., Sadeghi Mobarakeh, A., Alvari, H., & Shakarian, P. (2019). An End-to-End Framework to Identify Pathogenic Social Media Accounts on Twitter. In Proceedings - 2019 2nd International Conference on Data Intelligence and Security, ICDIS 2019 (pp. 128-135). [8855274] (Proceedings - 2019 2nd International Conference on Data Intelligence and Security, ICDIS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDIS.2019.00027