Academic Publications

1. Koliska, M., Moroney, E., & Beavers, D. (2023). “Trust Through Relationships in Journalism.” Journalism Studies. doi:10.1080/1461670X.2023.2209807

In this conceptual paper, my coauthors and I argue that the narrow focus on cognitive and rational evaluations of journalistic content in the existing literature on trust in news media should be expanded to explicitly incorporate emotional and social factors undergirding relationships (real or perceived) between journalists and audiences. We posit that a scholarly focus on relational trust can both lead to a better understanding of why and how audiences trust (or distrust) news media as well as suggest strategies for journalists to regain credibility amidst dwindling public trust in a polarized era.

2. Hochschild, J., & Beavers, D. (2022). “Learning from Experience: COVID-19 Conspiracy Theories and Their Implication for Democratic Discourse.” Social Science: International Quarterly, 89(3), 859-886. doi:10.1353/sor.2022.0042

In this article, Jennifer Hochschild and I utilize a series of weekly cross- sectional surveys conducted by YouGov on behalf of the Economist to explore adherence to conspiracy theories about the COVID-19 pandemic. We ask: How does local incidence of COVID-19 cases and fatalities affect support for several conspiracy theories about the origins and causes of the pandemic? We find that, though considerably noisy, the greater the impact that COVID-19 had on a respondent’s congressional district, the less likely he or she is to endorse a set of conspiracy theories. That this effect was most pronounced among respondents who were the least engaged with political discourse raises a troubling paradox for democratic polities.

You can hear more about this project in an interview for the Hopkins Press podcast. 

Selected Works in Progress

1. “Newspapers, Like Voters, Are Not Very Ideological” (with James M. Snyder Jr. and António Câmara)

Using data on newspaper and interest group endorsements on statewide ballot propositions, and the votes cast by citizens, we estimate the locations of newspapers, groups, and voters in a multi-dimensional policy space. Our early findings indicate that U.S. newspapers are less “ideological” than previously assumed, in the sense that their behavior cannot be easily predicted by a one-dimensional spatial model.

2. “The Political Origins and Contestation of Journalism’s Norm of Objectivity”

As part of my dissertation research, I'm reexamining the origins and evolution of American journalism's chief norm -- objectivity -- by foregrounding consideration of how political forces shaped its development and subsequent contestation. From its origins in response to WWI censorship and the growth of government news management to its reassessment amidst political criticisms both internal and external to the journalistic profession, I seek to demonstrate how journalism's changing adherence to the norm of objectivity can contribute to our understanding of dwindling trust in journalism specifically and public institutions more broadly. 

In my broader dissertation work, I hope to examine how public perceptions of the media -- including the professional norms and news-gathering procedures adopted by journalists -- impact political knowledge and behavior, as well as susceptibility to mis- and disinformation. 

3. “Local Experience, National Media, and Misperceptions of the COVID-19 Pandemic” (with Jennifer Hochschild)

Using the COVID-19 pandemic as a case study, Jennifer Hochschild and I seek to examine the conditions under which misperceptions sustain or attenuate in the presence of corrective information. Using 19 weekly cross-sectional surveys, county-level COVID-19 incidence data, and automated text analysis of national news coverage, we examine the interplay between national media attention and local experience with the pandemic and uncover conditions.

4. "Local News in the Crosshairs: Audience Perceptions of Algorithmic News" (with Kevin DeLuca)

Amid years of declines in American local news media, recent advances in artificial intelligence have brought about a host of algorithmic news sites purporting to cover local issues. In this project, we investigate whether audiences can discern between legitimate and algorithmic news sites and test a digital media literacy intervention designed to improve discernment.