Research

Research publications, pre-publication papers, and working papers

Published

Prasad, K., Martirosyan, V., et al. (2025). "The Business Climate in Maryland and the Impact of Federal Policy Changes on Maryland Businesses: A Survey of Maryland Businesses." Research Report, Robert H. Smith School of Business, UMD.

Contributed to the article with data gathering, cleaning, preprocessing, and analysis. Article published.

Pre-Publication Papers

Colelough, B., Martirosyan, V., et al. (2025). "Reproducibility in Computer Science: An Empirical Multi-Year Replication Study of Neuro-Symbolic AI." Under review at IEEE ICPC2026.

Co-first author, examining reproducibility in neuro-symbolic AI literature. We screened 5,497 papers from nine databases, narrowing the corpus to 1,365 candidates after deduplication and relevance filtering. I led quality control for the multi-stage reproduction protocol. arXiv pre-print coming soon.

Martirosyan, V., Kamdar, R. (2025). "How Verification Alters Cultural Signals in Employer Reviews." ArXiv Preprint.

First author on a study analyzing how employer verification on Glassdoor affects the cultural signals employees communicate in their reviews. We used natural language processing to examine differences in review content between verified and unverified employers. Read on arXiv!

Working Papers

Martirosyan, V., Jahani, E. (2025). "Misinformation Sharing Across Platforms and Its Determinants." Under review.

First author, examining misinformation sharing behavior across social media platforms in India, Pakistan, and Nigeria. Multi-country survey data from over 2,000 participants was collected from reputable local centers to test how platform type and content characteristics affect sharing intentions. Some key findings: sharing intent significantly lower on Social Networking Sites compared to Mobile Instant Messengers; high-plausibility content increased sharing intent; polarizing content decreased sharing intent; ideological extremity moderated sharing effects. arXiv pre-print coming soon.

Martirosyan, V., Clark, J. "Machine Learning in Information Systems: A Benchmarking Framework." In preparation.

First author on a paper developing a comprehensive benchmarking framework for evaluating machine learning applications in information systems research. We provide standardized methodologies and metrics for comparing ML approaches across IS domains, as well as an AI-in-the-loop framework to batch-analyze papers. Manuscript in preparation.