Detection and Analysis of Online Extremist Communities

Abstract

Online social networks have become a powerful venue for political activism. In many cases large, insular online communities form that have been shown to be powerful diffusion mechanisms of both misinformation and propaganda. In some cases these groups users advocate actions or policies that could be construed as extreme along nearly any distribution of opinion, and are thus called Online Extremist Communities (OECs). Although these communities appear increasingly common, little is known about how these groups form or the methods used to influence them. The work in this thesis provides researchers a methodological framework to study these groups by answering three critical research questions: How can we detect large dynamic online activist or extremist communities? What automated tools are used to build, isolate, and influence these communities? What methods can be used to gain novel insight into large online activist or extremist communities?

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2017
Accession Number
AD1157345

Entities

People

  • Matthew C Benigni

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • C4I
  • Cyber
  • Energy and Power Technologies
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Cognitive Systems Engineering
  • Computer Languages
  • Counterterrorism
  • Data Mining
  • Data Science
  • Heterogeneous Networks
  • Information Science
  • Knowledge Management
  • Machine Learning
  • Network Science
  • Political Movements
  • Social Media
  • Social Networking Services
  • Social Networks
  • Supervised Machine Learning
  • Terrorism
  • Terrorists

Readers

  • Neural Network Machine Learning.
  • Political Violence and Terrorism Studies.