Research Collaboration Areas

Digital Piracy Dynamics

Supply-side analysis and ecosystem evolution

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Digital Piracy Dynamics

Study supply-side analysis, release patterns, and ecosystem evolution. Analyze how content flows through digital networks, identify key players and patterns, and understand the dynamics of content distribution in peer-to-peer networks.

Cultural Analytics

Content diffusion and popularity trends

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Cultural Analytics

Investigate content diffusion patterns, popularity trends, and cultural impact studies. Understand how media content spreads across networks, what drives popularity, and how cultural phenomena emerge and evolve in digital spaces.

Policy Research

Anti-piracy measures and regulatory interventions

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Policy Research

Evaluate the effectiveness of anti-piracy measures and regulatory interventions. Analyze policy impacts, measure intervention success rates, and develop evidence-based recommendations for digital content protection strategies.

Machine Learning

Content classification and predictive modeling

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Machine Learning

Develop content classification systems, recommendation algorithms, and predictive models. Apply ML techniques to understand content patterns, predict popularity, and build intelligent systems for content analysis and discovery.

Network Analysis

P2P network structures and information flow

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Network Analysis

Analyze P2P network structures and information flow patterns. Study network topology, identify key nodes and communities, understand how information propagates through distributed networks, and model network dynamics.

Collaboration Formats

Joint publications and research projects

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Collaboration Formats

We welcome joint publications, collaborative data analysis, tool development, workshop organization, and student project supervision. Contact us to discuss specific collaboration opportunities and research proposals.

Research Team

Scott Seidenberger

PhD Candidate

Noah Pursell

Masters Student

Dr. Anindya Maiti

Assistant Professor

Getting Started

We hope you get in touch with us!

  1. Review the dataset: Check out our data access options and the potential collaboration areas
  2. Send a proposal: Email us a brief outline of your research interests and collaboration ideas (informal of course!)
  3. Schedule a meeting: We'll discuss potential synergies and next steps
  4. Student projects: Include your advisor's contact information for any thesis work

Contact us at: {seidenberger, am}@ou.edu