Abstract

The spread of harmful mis-information in social media is a pressing problem. We refer accounts that have the capability of spreading such information to viral proportions as 'Pathogenic Social Media' accounts. These accounts include terrorist supporters accounts, water armies, and fake news writers. We introduce an unsupervised causality-based framework that also leverages label propagation. This approach identifies these users without using network structure, cascade path information, content and user's information. We show our approach obtains higher precision (0.75) in identifying Pathogenic Social Media accounts in comparison with random (precision of 0.11) and existing bot detection (precision of 0.16) methods.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages57-64
Number of pages8
ISBN (Electronic)9781538657621
DOIs
StatePublished - May 25 2018
Event1st International Conference on Data Intelligence and Security, ICDIS 2018 - South Padre Island, United States
Duration: Apr 8 2018Apr 10 2018

Other

Other1st International Conference on Data Intelligence and Security, ICDIS 2018
CountryUnited States
CitySouth Padre Island
Period4/8/184/10/18

Keywords

  • Causality
  • Pathogenic Social Media
  • Social Bot
  • Terrorist Accounts
  • Water Armies

ASJC Scopus subject areas

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

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  • Cite this

    Shaabani, E., Guo, R., & Shakarian, P. (2018). Detecting pathogenic social media accounts without content or network structure. In Proceedings - 2018 1st International Conference on Data Intelligence and Security, ICDIS 2018 (pp. 57-64). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDIS.2018.00016