News personalization for peace: how algorithmic recommendations can impact conflict coverage

Abstract

Purpose The purpose of this paper is to develop a conceptual framework for assessing what are the possibilities and pitfalls of using algorithmic systems of news personalization – i.e. the tailoring of individualized news feeds based on users’ information preferences – for constructive conflict coverage in the context of peace journalism, a journalistic paradigm calling for more diversified and creative war reporting. Design/methodology/approach The paper provides a critical review of existing research on peace journalism and algorithmic news personalization, and analyzes the intersections between the two concepts. Specifically, it identifies recurring pitfalls of peace journalism based on empirical research on constructive conflict coverage and then introduces a conceptual framework for analyzing to what degree these pitfalls can be mediated – or worsened – through algorithmic system design. Findings The findings suggest that AI-driven distribution technologies can facilitate constructive war reporting, in particular by countering the effects of journalists’ self-censorship and by diversifying conflict coverage. The implementation of these goals, however, depends on multiple system design solutions, thus resonating with current calls for more responsible and value-sensitive algorithmic design in the domain of news media. Additionally, our observations emphasize the importance of developing new algorithmic literacies among journalists both to realize the positive potential of AI for promoting peace and to increase the awareness of possible negative impacts of new systems of content distribution. Originality/value The article particle is the first to provide a comprehensive conceptualization of the impact of new content distribution techniques on constructive conflict coverage in the context of peace journalism. It also offers a novel conceptual framing for assessing the impact of algorithmic news personalization on reporting traumatic and polarizing events, such as wars and violence.