Mary Lynn Young

From Mr. and Mrs. Outlier To Central Tendencies

This study examines the impact of computational journalism on the creation and dissemination of crime news. Computational journalism refers to forms of algorithmic, social scientific, and mathematical processes and systems for the production of news. It is one of a series of technological developments that have shaped journalistic work and builds on techniques of computer-assisted reporting and the use of social science tools in journalism. This paper uses the Los Angeles Times’ Homicide Report and its Data Desk as a case study to explore how technological adaptation occurred in this newsroom in the early twenty-first century. Our findings suggest that computational thinking and techniques emerged in a (dis)continuous evolution of organizational norms, practices, content, identities, and technologies that interdependently led to new products. Computational journalism emerges from an earlier and still ongoing turn to digital within broader organizational, technological, and social contexts. We place this finding in the local, situated context of the Homicide Report, one of the first crime news blogs to adopt computational journalism in North America.

The privileged normalization of marijuana use – an analysis of Canadian newspaper reporting, 1997–2007

The objective of this study was to systematically examine predominant themes within mainstream media reporting about marijuana use in Canada. To ascertain the themes present in major Canadian newspaper reports, a sample (N = 1999) of articles published between 1997 and 2007 was analyzed. Drawing from Manning’s theory of the symbolic framing of drug use within media, it is argued that a discourse of ‘privileged normalization’ informs portrayals of marijuana use and descriptions of the drug’s users. Privileged normalization implies that marijuana use can be acceptable for some people at particular times and places, while its use by those without power and status is routinely vilified and linked to deviant behavior. The privileged normalization of marijuana by the media has important health policy implications in light of continued debate regarding the merits of decriminalization or legalization and the need for public health and harm reduction approaches to illicit drug use.

Finding the Data Unicorn

Understanding how data and computational journalism are affecting news norms, practices and organizations is essential for journalists and researchers. We explore the development of data journalism in Canada through interviews with 17 data journalists and freelancers at six of the country’s largest legacy news organizations. Our central question is how these journalistic identities are both shaping intra- and inter-organizational and professional boundaries and being shaped by them. We draw from systems thinking approach to understanding media hybridity across time and place as well as the traditional sociology of news to assess technological adaptation and agency in this emerging domain. Findings suggest that encounters between emergent and legacy logics have created a hierarchy of hybrid cultures. Two organizations—the public broadcaster and a legacy print organization—showed early stage evidence of blended techno cultures similar to some in the United States. In others there were tensions with professional labeling, resourcing, protective economic strategies and labor policies, limiting options to mobilize power and experiment with technological adaptation in more generative ways. These shifts were occurring within a context of increased cooperation within, across and beyond traditional organizations, pointing to a new degree of networked collaboration in Canadian journalism.

What Makes for Great Data Journalism

This study examines the quality of winners and finalists in major national and international data journalism awards. We completed a content analysis of data projects submitted by Canadian media to three journalism associations—the Online News Association, the Global Editors Network and the Canadian Association of Journalists—as far back as the first award in this category in 2012. Our research addresses how journalists executed what could be considered excellent data journalism. Our findings point to a lack of accepted standards regarding what is considered as excellence. The quality of the projects was limited by two key factors: the use of free online options such as Google Maps that were not easily customizable; and the number of practitioners who worked on the data projects largely within traditional journalism frameworks. The most used visual elements were dynamic maps, graphs and video. With respect to interactivity, all but one of the projects contained an interactive element. The most popular interaction techniques were inspection and filtering, considered entry-level techniques in the field of information visualization. These techniques suggest a need for collaborative interdisciplinary approaches to data journalism, and further study on the implications of tools such as Google Maps on practice.

A computer-assisted approach to filtering large numbers of documents for media analyses

Media analysts are challenged to acquire selections of documents that are representative of their topics of interest. Conventional search and selection processes are often constrained because of an inability to efficiently filter large amounts of potentially relevant documents and thus pose the risk of introducing bias. We describe a computer-assisted approach to increase the probability of identifying all articles relevant to a topic (in this case, marijuana), and provide an evaluation of its effectiveness in reducing bias while minimizing time expenditure. Using our system, we filtered 23,755 articles in 24.4 h. Relative to conventional processes, a substantial reduction in bias was achieved. Our system significantly reduced the risk of bias while retaining efficiency and accuracy in document selection.

When gender, colonialism, and technology matter in a journalism startup:

This article is based on an ethnographic study of a women-led journalism startup, identified as a digital and data innovator in North America. Studies of journalism startups have generally focused on growth in the startup space and claims to technological innovation, finding a persistence of traditional norms and practices. Feminist media scholars have not tended to engage in this area of study, focusing more on newsroom sociology and media representations, despite a long history of feminist Science and Technology Studies critique of other technical professions such as engineering and computer science. This study adds to our understanding of journalism startups by situating this ethnography within feminist, postcolonial, and Science and Technology Studies approaches. Our findings suggest the persistence of professional, industry, and economic constraints mapped on to gender, gendered understandings of innovation, and technology in journalism – as well as possibilities to transform them. We argue that gend...