Media Studies and Applied Ethics (special issue)

Deadline (abstracts): 13 December 2022

Edited by Ana Milojevic (University of Bergen)

Datafication is changing every aspect of our society including journalism as one of the important fundaments of democracy. Following the news production phases (observation, production, distribution, and news consumption) Loosen (2018:4) distinguishes between four forms of datafied journalism: data-based journalism, alogrithmed journalism, automated journalism, and metrics-driven journalism. Different aspects of data driven changes in journalism have been examined in all those forms during last decades, but many blind spots are still to be filled. Therefore, the main aim of this special issue is to put audiences in the forefront of examining different forms of journalism datafication.

Namely, data journalism as the fast-growing phenomena has been attracting scholarly attention. However, most of the research has been focusing on identifying characteristics of data journalism as the emerging subfield (genres, methods, storytelling techniques) and its integration into organizations, practices, and education worldwide (e.g. Bhaskaran, Kashyap & Mishra, 2022; Fink & Anderson, 2015; Munoriyarwa, 2022; Young, Hermida, & Fulda, 2018; Wu, 2022), while far less is known about audience relation to data journalism.

In the strand of the algorithmic journalism research, studies of user interactions with algorithms have been more prominent and diversified, including user perceptions of news personalization process (Monzer, 2020), experiences of news recommender systems (Wieland, 2021), and satisfaction with algorithmic news selections (Swart, 2021; Thurman et al. 2019). However, as Shin (2022: 1168) underlines, “little is known about the ways through which readers understand and actualize the potential for trust or affordances in algorithmic journalism”.

Also, significant body of research considers audiences in form of audience analytics and metrics as central for journalism transformation, including journalistic roles (Belair-Gagnon, Zamith, and Holton, 2020), news values (Kristensen, 2021), news selection (Lamot and Van Aelst, 2020), and journalistic norms and routines (Ekström, Ramsälv and Westlund, 2021). However, this area of research is mainly focused on editors’ and journalists’ work and decision-making processes. Much less attention has been given to data-analysts as growingly important actors in media, companies providing analytics to media, existing metrics and infrastructures for audience datafication.

Therefore, we invite submissions that theorize or empirically study the role of audience datafication in journalism, as well as audience interaction and engagement with data-based and algorithmic journalism. More precisely, studies that aim to answer: How is data journalism perceived, consumed, and valued in different contexts? What kind of audience needs data journalism gratifies? Does data journalism foster audience engagement? Second, we seek submissions that examine how users perceive algorithmic features and experience algorithm systems in the context of algorithmic journalism. Third, we welcome papers that focus on the role of various technological agents and non-journalist actors that intervene in the use of audience analytics and metrics in newsrooms.

Timeline:

Abstract deadline: 13 December 2022

Manuscript deadline: 31 March 2023

No Payment from authors will be required. More information on the call:

https://izdanja.filfak.ni.ac.rs/casopisi/media-studies-and-applied-ethics

For further details please contact Ana Milojevic

(ana.milojevic@gmail.com)

References:

Belair-Gagnon, V., Zamith, R., & Holton, A. E. (2020). Role orientations and audience metrics in newsrooms: An examination of journalistic perceptions and their drivers. Digital Journalism, 8(3), 347-366.

Bhaskaran, H., Kashyap, G., & Mishra, H. (2022). Teaching Data Journalism: A Systematic Review. Journalism Practice, 1-22.

Ekström, M., Ramsälv, A., & Westlund, O. (2021). Data-driven news work culture: Reconciling tensions in epistemic values and practices of news journalism. Journalism, DOI: 14648849211052419.

Fink, K., & Anderson, C. W. (2015). Data Journalism in the United States: Beyond the “usual suspects”. Journalism studies, 16(4), 467-481.

Kristensen, L. M. (2021). Audience Metrics: Operationalizing News Value for the Digital Newsroom. Journalism Practice, DOI: 10.1080/17512786.2021.1954058

Lamot, K., & Van Aelst, P. (2020). Beaten by Chartbeat? An experimental study on the effect of real-time audience analytics on journalists’ news judgment. Journalism Studies, 21(4), 477-493.

Monzer, C., Moeller, J., Helberger, N., & Eskens, S. (2020). User perspectives on the news personalisation process: Agency, trust and utility as building blocks. Digital Journalism, 8(9), 1142-1162.

Munoriyarwa, A. (2022). Data journalism uptake in South Africa’s mainstream quotidian business news reporting practices. Journalism, 23(5), 1097-1113.

Shin, D. (2022). Expanding the role of trust in the experience of algorithmic journalism: User sensemaking of algorithmic heuristics in Korean users. Journalism Practice, 16(6), 1168-1191.

Swart, J. (2021). Experiencing algorithms: How young people understand, feel about, and engage with algorithmic news selection on social media. Social media+ society, 7(2), 20563051211008828.

Thurman, N., J. Moeller, N. Helberger, and D. Trilling. 2019. “My Friends, Editors, Algorithms, and I.” Digital Journalism 7 (4): 447–469.

Wieland, M., Von Nordheim, G.(2021). One Recommender Fits All? An Exploration of User Satisfaction With Text-Based News Recommender Systems. Media and Communication, 9(4), 208-221.

Wu, S. (2022). Asian Newsrooms in Transition: A Study of Data Journalism Forms and Functions in Singapore’s State-Mediated Press System. Journalism Studies, 23(4), 469-486.

Young, M. L., Hermida, A., & Fulda, J. (2018). What makes for great data journalism? A content analysis of data journalism awards finalists 2012–2015. Journalism practice, 12(1), 115-135.