Digital journalism 

About: Digital journalism is an academic journal published by Taylor & Francis. The journal publishes majorly in the area(s): Journalism & Social media. It has an ISSN identifier of 2167-0811. Over the lifetime, 881 publications have been published receiving 25315 citations. The journal is also known as: digijournalism & cyberjournalism.

Defining “Fake News”: A typology of scholarly definitions

Abstract: This paper is based on a review of how previous studies have defined and operationalized the term “fake news.” An examination of 34 academic articles that used the term “fake news” between 2003 and...

Algorithmic Accountability: Journalistic investigation of computational power structures

Abstract: Every day automated algorithms make decisions that can amplify the power of businesses and governments. Yet as algorithms come to regulate more aspects of our lives, the contours of their power can remain difficult to grasp. This paper studies the notion of algorithmic accountability reporting as a mechanism for elucidating and articulating the power structures, biases, and influences that computational artifacts exercise in society. A framework for algorithmic power based on autonomous decision-making is proffered and motivates specific questions about algorithmic influence. Five cases of algorithmic accountability reporting involving the use of reverse engineering methods in journalism are then studied and analyzed to provide insight into the method and its application in a journalism context. The applicability of transparency policies for algorithms is discussed alongside challenges to implementing algorithmic accountability as a broadly viable investigative method.

Fake News and The Economy of Emotions: Problems, causes, solutions

Abstract: This paper examines the 2016 US presidential election campaign to identify problems with, causes of and solutions to the contemporary fake news phenomenon. To achieve this, we employ textual analys...

Quantitative analysis of large amounts of journalistic texts using topic modelling

Abstract: The huge collections of news content which have become available through digital technologies both enable and warrant scientific inquiry, challenging journalism scholars to analyse unprecedented amounts of texts. We propose Latent Dirichlet Allocation (LDA) topic modelling as a tool to face this challenge. LDA is a cutting edge technique for content analysis, designed to automatically organize large archives of documents based on latent topics, measured as patterns of word (co-)occurrence. We explain how this technique works, how different choices by the researcher affect the results and how the results can be meaningfully interpreted. To demonstrate its usefulness for journalism research, we conducted a case study of the New York Times coverage of nuclear technology from 1945 to the present, partially replicating a study by Gamson and Modigliani. This shows that LDA is a useful tool for analysing trends and patterns in news content in large digital news archives relatively quickly.

Twitter as a reporting tool for breaking news

Abstract: This study focuses on journalists Paul Lewis (The Guardian) and Ravi Somaiya (The New York Times), the most frequently mentioned national and international journalists on Twitter during the 2011 UK summer riots. Both actively tweeted throughout the four-day riot period and this article highlights how they used Twitter as a reporting tool. It discusses a series of Twitter conventions in detail, including the use of links, the taking and sharing of images, the sharing of mainstream media content and the use of hashtags. The article offers an in-depth overview of methods for studying Twitter, reflecting critically on commonly used data collection strategies, offering possible alternatives as well as highlighting the possibilities for combining different methodological approaches. Finally, the article makes a series of suggestions for further research into the use of Twitter by professional journalists.