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The Explanatory Journalism Impact and Update (XJO) project explores the character and influence of academic explanatory journalism. This project runs under University of British Columbia's Global Journalism Innovation Lab, external link, funded by a SSHRC Partnership Grant, external link. In our research, we examine The Conversation, external link, an international journalism network where researchers publish explanatory news and current events articles related to their own areas of study. By uncovering what contributes to effective explanation, this research will identify strategies that seem to make The Conversation unique for the future of digital journalism and knowledge translation. 

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XJO Research Projects

Two smarphone screens displaying news articles about COVID-19

Crisis Communication in Explanatory and Alternative Journalism Outlets

Project Lead: Dr. Sibo Chen

Student Researcher: Sarah Ostapchuk

This project investigates the coverage of COVID-19 in varying media outlets from a crisis communication lens. The goal of this research is to compare how The Conversation's handling of crisis communication differs from alternative media outlets in their discussion of COVID-19 as a global crisis through a comparative analysis.

Cityscape with imaginary lines representing an interconnected digital world

Frames of AI & Robotics in Explanatory Journalism

Project Lead: Dr. Frauke Zeller

Student Researchers: Charlotte Crawford, Lauren Dwyer, Hayden Godfrey, Leah HoniballSofia Rodriguez-Garzon

This project investigates how the COVID-19 health crisis has impacted media representations and dominant media frames of technology, AI, and robotics. Public opinion plays an important role in the initial design and development of emerging technologies, as well as their general uptake among society and its institutions. The way we imagine, observe, and engage with these technologies is steeped in conventional narratives that reflect our broader hopes, anxieties, and (mis)conceptions about robots, AI-powered technology, and the populations around us. Ideas about security, control, data privacy, and job losses due to automation, to name a few, often permeate the way we think about and discuss robotics and AI.  

Map of COVID-19 hot spots in Europe

Data Visualizations in Explanatory Journalism

Project Lead: Dr. Jessica Mudry

Student Researcher: Grace EsfordDaryn Tyndale

For the casual news reader, numerical data is often viewed as a source of impartial, scientific evidence. Because of these associations with objectivity, including data visualizations like charts and graphs in a news article might lend the piece a sense of credibility—regardless of whether the information depicted is actually relevant to the text.

Article in a French-language newspaper

Communication of COVID-19 Variables in Explanatory Journalism

Project Lead: Dr. John Shiga

Student Researcher: Ambika Argawal

This project uses quantitative corpus analysis to investigate how the reproduction number (R) of COVID-19 is communicated in The Conversation Canada in comparison to more traditional forms of journalism found in Canadian news media. The reproduction number of a virus quantifies how infectious a disease is. R has been one of the leading variables used to qualify COVID-19’s contagiousness. R is used to explain the threat posed by COVID-19 and the actions taken by health authorities to contain the disease.

Student taking notes from a laptop

A Codebook for Uptake of Explanatory Journalism

Project Lead: Dr. Catherine Schryer

Student Researchers: Daryn Tyndale, Sam Shaftoe

This project analyzes explanatory journalism as an emerging genre. Drawing on a wide range of communication disciplines, including classical rhetorical studies, journalism studies, genre theory, and theories of knowledge translation, Dr. Schryer’s team is working to identify the essential features and strategies that characterize explanatory journalism as it is being practiced on The Conversation and develop a better understanding of how explanation functions in journalistic writing.

Woman sits in bed, taking notes from a laptop

The Audience Impact of The Conversation Articles

Project Lead: Dr. Charles Davis

Student Researchers: Saniya Rashid, Stuart Duncan

This project investigates how explanatory journalism is practiced throughout The Conversation and the relationship between explanatory journalism and audience impact. Taking a mixed-methods approach, Dr. Davis’ team is utilizing natural language processing tools like LIWC (Linguistic Inquiry and Word Count) to identify high-level trends in The Conversation articles, complemented by qualitative readings that confirm the automated results and highlight important nuances. Identifying common features of The Conversation stories allows for better  understanding of The Conversation’s particular brand of journalism, and the concept of explanatory journalism more broadly.

Lines of computer code in multicoloured text

Using Machine Learning to Detect Explanatory Strategies

Project Lead: Dr. Robert Clapperton

Student Researcher: Amin Mirlohi

This project is working to create a custom machine learning model that can detect strategies of deliberative rhetoric used in the explanatory journalism content of The Conversation. The deliberative, epideictic, and forensic modes of rhetoric are strategies that can be used by journalists to shape their content, ultimately impacting the audience’s understanding of the situation being discussed. These modes are temporal in nature, with forensic rhetoric focusing on establishing facts about the past, epideictic rhetoric stating proclamations about the present, and deliberative rhetoric outlining possible futures while attempting to convince audiences to work towards (or against) these possible futures.