[Commlist] Call for Papers: Governing the Algorithmic Distribution of News

Call for Papers: Governing the Algorithmic Distribution of News

Editors: Sara Bannerman (McMaster University) and James Meese 
(University of Technology Sydney)

In January 2018, Facebook declared that it would no longer prioritise 
news content in its NewsFeed. Instead, it would surface posts from 
‘friends and family’, with the goal of bringing ‘people closer together’ 
(Mosseri, 2018). Facebook had stopped promoting particular forms of news 
before (like clickbait headlines) but they had always retained a broad 
commitment to distributing news content. However, the change in 2018 
represented a major pivot for a platform that had increasingly become a 
central intermediary for online news distribution. In response, 
digital-first publications, who had staked their business model on 
Facebook’s ability to surface news to audiences, started to lay off 
staff in significant numbers. These new disruptive news enterprises 
(like Buzzfeed and Mic) were supposed to usher in a new future for news. 
However, it appeared that their business models were as unstable as 
those of their print predecessors.

These recent developments have not gone unnoticed by governments. 
Policymakers and politicians across the world are starting to examine 
the role that platforms and algorithms play in the distribution of news. 
Inquiries in Australia, the United Kingdom, Canada and elsewhere have 
explored the consequences of the algorithmic distribution of news. 
Alongside these national inquiries, a broader international discussion 
has focused on the apparent rise in disinformation and the increasingly 
partisan nature of political discourse. This discussion has intensified 
recently, leading to the formation of an International Grand Committee 
on Big Data, Privacy and Democracy composed of elected officials from 
governments around the world.

This edited collection will respond to this international policy moment 
and examine the challenges posed by the algorithmic distribution of 
news. It will critically assess recent media policy developments in this 
space and explore the broader economic, political and industrial 
transformations associated with algorithmic distribution. In doing so, 
we aim to offer a comprehensive account of this moment of institutional 
change, which has significantly altered the distribution and consumption 
of news (see Nielsen 2018).

The book will be split into two sections. The first section will consist 
of thematic chapters (5 - 6,000 words) and the second section will 
feature shorter case studies (3 - 4,000 words) describing and analysing 
recent policy developments related to algorithmic distribution in 
particular countries. We are currently in discussions with interested 
publishers and seeking contributions for both sections.

Possible topics include (but are not limited to):

- International governance of the algorithmic distribution of news, 
including the formation and operation of the International Grand Committee;

- Measures to support media diversity in light of algorithmic 
distribution, including measures to support local, Indigenous, 
alternative, independent, ethnic, women’s and minority news media;

- Case studies of countries (for section two): how have particular 
countries approached regulatory problems in light of the algorithmic 
distribution of news?

- Subsidies and tax exemptions that respond to the algorithmic 
distribution of news;

- Discussions of regulations intended to ensure the objectivity and/or 
transparency of search and recommendation algorithms;

- Regulatory measures that respond to layoffs and closures of news outlets;

- Intersections between copyright law and news aggregation (such as the 
EU’s Article 11, the ‘Google News tax;’

- The relationship between news, platforms, and competition law;

- Regulation of targeted advertising in relation to news;

- Histories of early forays into online (or social) news distribution;

- Analyses of innovative forms of news distribution;

- Civic risks associated with algorithmic distribution (or online 
engagement);and

- Detailed analyses of relevant inquiries or reform proposals.

If you are interested in contributing to either section, please send a 
short chapter or case study proposal (of about 400 words) and a 
biography (150 words) by the 25th of October 2019 to 
(james.meese /at/ uts.edu.au) and (banners /at/ mcmaster.ca).

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