Abstract

Pathogenic Social Media (PSM) accounts such as terrorist supporters exploit large communities of supporters for conducting attacks on social media. Early detection of these accounts is crucial as they are high likely to be key users in making a harmful message 'viral'. In this paper, we make the first attempt on utilizing causal inference to identify PSMs within a short time frame around their activity. We propose a time-decay causality metric and incorporate it into a causal community detection-based algorithm. The proposed algorithm is applied to groups of accounts sharing similar causality features and is followed by a classification algorithm to classify accounts as PSM or not. Unlike existing techniques that take significant time to collect information such as network, cascade path, or content, our scheme relies solely on action log of users. Results on a real-world dataset from Twitter demonstrate effectiveness and efficiency of our approach. We achieved precision of 0.84 for detecting PSMs only based on their first 10 days of activity; the misclassified accounts were then detected 10 days later.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Intelligence and Security Informatics, ISI 2018
EditorsDongwon Lee, Ghita Mezzour, Ponnurangam Kumaraguru, Nitesh Saxena
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages169-174
Number of pages6
ISBN (Electronic)9781538678480
DOIs
StatePublished - Dec 24 2018
Event16th IEEE International Conference on Intelligence and Security Informatics, ISI 2018 - Miami, United States
Duration: Nov 9 2018Nov 11 2018

Other

Other16th IEEE International Conference on Intelligence and Security Informatics, ISI 2018
CountryUnited States
CityMiami
Period11/9/1811/11/18

Keywords

  • Causal inference
  • Community detection
  • Early identification
  • Pathogenic social media accounts

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Communication

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  • Cite this

    Alvari, H., Shaabani, E., & Shakarian, P. (2018). Early identification of pathogenic social media accounts. In D. Lee, G. Mezzour, P. Kumaraguru, & N. Saxena (Eds.), 2018 IEEE International Conference on Intelligence and Security Informatics, ISI 2018 (pp. 169-174). [8587339] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISI.2018.8587339