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This study investigates perceptions of the use of online tracking, a passive data collection method relying on the automated recording of participant actions on desktop and mobile devices, for studying information behavior. It scrutinizes folk theories of tracking, the concerns tracking raises among the potential participants and design mechanisms that can be used to alleviate these concerns.

This study uses focus groups composed of university students (n = 13) to conduct an in-depth investigation of tracking perceptions in the context of information behavior research. Each focus group addresses three thematic blocks: (1) views on online tracking as a research technique, (2) concerns that influence participants' willingness to be tracked and (3) design mechanisms via which tracking-related concerns can be alleviated. To facilitate the discussion, each focus group combines open questions with card-sorting tasks. The results are analyzed using a combination of deductive content analysis and constant comparison analysis, with the main coding categories corresponding to the thematic blocks listed above.

The study finds that perceptions of tracking are influenced by recent data-related scandals (e.g. Cambridge Analytica), which have amplified negative attitudes toward tracking, which is viewed as a surveillance tool used by corporations and governments. This study also confirms the contextual nature of tracking-related concerns, which vary depending on the activities and content that are tracked. In terms of mechanisms used to address these concerns, this study highlights the importance of transparency-based mechanisms, particularly explanations dealing with the aims and methods of data collection, followed by privacy- and control-based mechanisms.

The study conducts a detailed examination of tracking perceptions and discusses how this research method can be used to increase engagement and empower participants involved in information behavior research.

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.

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.

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.

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.