Nick Hagar

I research collective attention problems on digital platforms, with a focus on news media. I’m concerned with attention inequality and its impact on digital marketplaces, models of collective information retrieval, and algorithms’ role in shaping attention.

Current projects include simulating collective attention to popular items on Netflix, modeling cumulative advantage in linking behavior on Substack, and building a framework for more robust sampling of news archives, among others. My work uses computational methods, such as simulation, network analysis, and natural language processing.

Before grad school, I worked in audience development and analytics at Fusion Media Group, Digiday, Pacific Standard and the Dallas Morning News. I’ve written for Pacific Standard, the Christian Science Monitor, McSweeney’s Internet Tendency and others. I graduated from the Medill School of Journalism at Northwestern University in 2017 and am originally from Fort Wayne, Indiana.

news

Sep 8, 2021	I went on the Science of Politics podcast to talk about our recent New Media & Society paper.
Aug 16, 2021	Our recent New Media & Society paper was written up in Mark Coddington and Seth Lewis’ RQ1 newsletter.
Jul 12, 2021	Our recent New Media & Society paper was written up in Der Tagesspiegel.
May 30, 2021	“Writer movements between news outlets reflect political polarization in media”, with Johannes Wachs and Professor Ágnes Horvát, was accepted to New Media & Society.
Apr 8, 2021	I’ll be presenting “Writer movements between news outlets reflect political polarization in media” at Networks 20201.

selected publications

M&C

Optimizing Content with A/B Headline Testing: Changing Newsroom Practices

Hagar, Nick, and Diakopoulos, Nicholas

Media and Communication 2019

Abs PDF

Audience analytics are an increasingly essential part of the modern newsroom as publishers seek to maximize the reach and commercial potential of their content. On top of a wealth of audience data collected, algorithmic approaches can then be applied with an eye towards predicting and optimizing the performance of content based on historical patterns. This work focuses specifically on content optimization practices surrounding the use of A/B headline testing in newsrooms. Using such approaches, digital newsrooms might audience-test as many as a dozen headlines per article, collecting data that allows an optimization algorithm to converge on the headline that is best with respect to some metric, such as the click-through rate. This article presents the results of an interview study which illuminate the ways in which A/B testing algorithms are changing workflow and headline writing practices, as well as the social dynamics shaping this process and its implementation within US newsrooms.
NMS

Writer Movements between News Outlets Reflect Political Polarization in Media

Hagar, Nick, Wachs, Johannes, and Horvát, Emőke-Ágnes

New Media & Society 2021

Abs PDF

Digital news outlets rely on a variety of outside contributors, from freelance journalists, to political commentators, to executives and politicians. These external dependencies create a network among news outlets, traced along the contributors they share. Using connections between outlets, we demonstrate how contributors’ publishing trajectories tend to align with outlet political leanings. We also show how polarized clustering of outlets translates to differences in the topics of news covered and the style and tone of articles published. In addition, we demonstrate how contributors who cross partisan divides tend to focus on less explicitly political topics. This work addresses an important gap in the media polarization literature, by highlighting how structural factors on the production side of news media create an ecosystem shaped by political leanings, independent of the priorities of any one person or organization.