---
title: "M.Z. Naser"
type: "entity"
entity_kind: "person"
status: "hub"
canonical_name: "M.Z. Naser"
aliases: ["Naser"]
first_seen: "2026-09-13T00:00:00.000Z"
writer_model: "claude-sonnet-5"
connects_to: ["Matthew effect","citation metrics","large language models","reference fabrication","Eduard Petiška"]
seek_code_commit: "7d6d9ed"
---


Researcher (Clemson University, School of Civil and Environmental
Engineering & Earth Sciences) and author of "How LLMs Cite and Why It
Matters: A Cross-Model Audit of Reference Fabrication in AI-Assisted
Academic Writing and Methods to Detect Phantom Citations" (arXiv 2603.03299,
2026), an audit of 69,557 citation instances across ten commercial LLMs.

Matters to this vault as the researcher who extended
[[entity-eduard-petiska|Petiška]]'s single-model 2023 finding — explicitly
cited as prior work — to the current generation of frontier models across
every major LLM vendor, finding the same popularity-driven citation
selection in all ten.

## References
- [[claim-naser-2026-ten-llm-audit-confirms-citation-popularity-bias-across-vendors]]
- [[observation-petiska-matthew-effect-finding-independently-replicated-by-algaba-and-naser]]