Press credentialing practices are a vital, yet understudied site of scholarly research on journalistic norms and practices. Press credentialing not only structures internal professional hierarchies, but they also signify the boundaries of the journalistic field itself. This paper explores the legal and theoretical implications of press credentialing to cover the United States Congress, drawing on the concepts of boundary work and journalistic authority to demonstrate the material impact of the space between fields on professional legitimation in journalism. Using WorldNetDaily (WND) as a case study, I argue that the Standing Committee of Correspondents (SCC) occupies a hybrid boundary zone between the journalistic and political fields, generating a unique tension in First Amendment jurisprudence that places journalists in a paradoxical role as both the professional embodiments of free speech and its constitutional steward. The resulting jurisdictional conflict between the SCC and WND extends the relational model of journalistic authority by articulating how journalist-state relations can fundamentally augment the process of legitimation at its fuzzy boundaries. The relevance and implications for press credentialing practices in the digital age are discussed.

Some of the most pioneering work in our field is occurring where emerging computational approaches are meeting time series analytic techniques. Combining these methods is helping scholars improve our understanding of phenomena as varied as news and issue attention cycles, physiological responses to communication exposure, changes in mass opinion, and the dynamics between social media and legacy news media. In this article, we summarize the current state of computational communication science techniques to generate sequential data for use in time series analysis and suggest directions for further development. In particular, we consider the long-standing place of temporal dynamics for our field’s main theories; overview recent work combining computational science with time series analysis; present narrative accounts of two major research programs in this area; and review techniques of time series analysis, including major concerns for communication researchers working in the area.

This study focuses on the outpouring of sympathy in response to mass shootings and the contestation over gun policy on Twitter from 2012 to 2014 and relates these discourses to features of mass shooting events. We use two approaches to Twitter text analysis—hashtag grouping and supervised machine learning (ML)—to triangulate an understanding of intensity and duration of “thoughts and prayers,” gun control, and gun rights discourses. We conduct parallel time series analyses to predict their temporal patterns in response to features of mass shootings. Our analyses reveal that while the total number of victims and child deaths consistently predicted public grieving and calls for gun control, public shootings consistently predicted the defense of gun rights. Further, the race of victims and perpetrators affected the levels of public mourning and policy debates, with the loss of black lives and the violence inflicted by white shooters generating less sympathy and policy discourses.

Populism, as many have observed, is a communication phenomenon as much as a coherent ideology whose mass appeal stems from the fiery articulation of core positions, notably hostility towards “others,” bias against elites in favor of “the people,” and the transgressive delivery of those messages. Yet much of what we know about populist communication is based on analysis of candidate pronouncements, the verbal message conveyed at political events and over social media, rather than transgressive performances—the visual and tonal markers of outrage—that give populism its distinctive flair. The present study addresses this gap in the literature by using detailed verbal, tonal, and nonverbal coding of the first U.S. presidential debate of 2016 between Donald Trump and Hillary Clinton to show how Trump’s transgressive style—his violation of normative boundaries, particularly those related to protocol and politeness, and open displays of frustration and anger—can be operationalized from a communication standpoint and used in statistical modeling to predict the volume of Twitter response to both candidates during the debate. Our findings support the view that Trump’s norm-violating transgressive style, a type of political performance, resonated with viewers significantly more than Clinton’s.

Academic Experience


Project Assistant

Center for Communication and Civic Renewal, University of Wisconsin-Madison

Sep 2018 – Present Madison, WI
This project examines how growing polarization and fragmentation in the Wisconsin media ecology, as reflected in talk radio, local news, political advertising and social media, contributed to ideological and partisan political divides in the state. It will also study under what conditions the flow of information in the Wisconsin media ecology encourage citizens across the ideological spectrum to retrench into “echo chambers” that amplify highly partisan messages of party leaders and pundits within state politics. Responsibilities include database construction and maintenance, web scraping, statistical modelling, survey construction, data visualization, time series modelling, grant writing.

Teaching Assistant

School of Journalism and Mass Communication, University of Wisconsin-Madison

Sep 2016 – Jan 2018 Madison, WI
Responsible for leading discussion sections in courses ranging from 15 to 100+ students in theory, methods, law, and practice in journalism and mass communication. Primarily responsible for grading, classroom supervision, final project oversight, course administration, and one-on-one student coaching.

Research Assistant

Mass Communication Research Center, University of Wisconsin-Madison

Sep 2016 – Jan 2018 Madison, WI
Responsible for hourly project work, including sub-group coordination, dataset construction, management and maintenance as well as writing code for descriptive analyses, statistical modelling, and training machine learning algorithms to classify news coverage and Twitter content. Projects focus on multi-layered time-series modeling of media ecologies surrounding contentious political issues and events like presidential debates, political campaigns, and mass shootings.

Teaching Experience

Theory, Method, Law, and Practice

I’ve taught in a number of classroom settings since 2009, instructing and mentoring students as they learn how to become better researchers, writers, and advocates.

Wake Forest University:

  • COM 220: Empirical Research in Communication

University of Wisconsin-Madison:

Summer High School Debate Institutes