News personalization for peace: How algorithmic content distribution can impact conflict coverage

Bastian, Mariella; Makhortykh, Mykola; Dobber, Tom (8 July 2019). News personalization for peace: How algorithmic content distribution can impact conflict coverage (Unpublished). In: IAMCR 2019. Madrid, Spain. 07.07.-11.07.2019.

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In our paper we discuss how the increasing use of AI-driven systems of content distribution impacts the ways in which people are exposed to news about wars and conflicts. Specifically, we discuss the place of algorithmic personalization – i.e. the tailoring of individualized
news feeds based on users’ information preferences – in the framework of peace journalism (PJ), a journalistic paradigm calling for more diversified and creative war reporting. Using a conceptual approach, we discuss how the deployment of news personalization can address existing pitfalls of PJ paradigm, and develop a theoretical framework for analyzing how algorithmic system designs can facilitate constructive conflict coverage.
To achieve these purposes, we provide a thorough review of existing research on peace journalism and algorithmic personalization, and analyze the intersections between the two concepts. Specifically, we identify recurring pitfalls of peace journalism based on empirical research on constructive conflict coverage, and then introduce a conceptual framework for identifying to what degree these pitfalls can be mediated – or worsened – through algorithmic system design. By doing so, we address the following research questions: In which ways can AI-driven distribution technologies influence the realization of central objectives of peace journalism? Which ethical concerns arise from the use of algorithmic personalization for constructive conflict coverage? And what are possible technical solutions through which algorithmic personalization can contribute to strengthening peace?
Through our analysis we identified five major aspects of conflict coverage which can be affected by algorithmic content personalization: relativity, objectivity, diversity, transparency, and engagement. For each of these aspects we examined potential opportunities and pitfalls related to the introduction of news personalization systems and connected them to concrete algorithmic designs (e.g. context-aware recommender algorithms in the case of relativity or point of interest cross-referencing for objectivity). Based on our examination, we propose a conceptual framework for assessing interactions between AI-driven distribution techniques and peace journalism.
Our 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.

Item Type:

Conference or Workshop Item (Paper)


03 Faculty of Business, Economics and Social Sciences > Social Sciences > Institute of Communication and Media Studies (ICMB)

UniBE Contributor:

Makhortykh, Mykola


300 Social sciences, sociology & anthropology
000 Computer science, knowledge & systems
000 Computer science, knowledge & systems > 070 News media, journalism & publishing




Mykola Makhortykh

Date Deposited:

30 Jul 2019 09:13

Last Modified:

13 Feb 2024 07:58

Uncontrolled Keywords:

algorithms personalization conflict coverage peace journalism


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