Professional Challenges to Objective Journalism: Personalization and Creation of “Filter Bubbles”
DOI:
https://doi.org/https://doi.org/10.37075/RP.2025.5.05Keywords:
Social media, Personalization, Algorithms, Journalism, Filter bubblesAbstract
This article examines the impact of personalization algorithms used in social networks on the creation of information bubbles and their effect on the work of journalists. The study aims to clarify whether these algorithms limit the perspectives journalists see in these digital platforms, and how this affects the daily work of content creators in the media. The study's methodology includes a non-representative survey among journalists working in Bulgarian print, electronic, and online media. The main hypothesis is that personalization algorithms limit journalists' direct access to different perspectives, which is why these services cannot be relied on as a primary source of reliable information. The creation of information bubbles has become a challenge for objective journalism and requires much more active work in the direction of verification and search for information.References
Agichtein, E. and Brill, E. and Dumais, S. (2006). Improving web search ranking by incorporating user behavior information. Proceedings of the 29th Annual International ACM Sigir Conference on Research and Development in Information Retrieval, 19-26. DOI: https://doi.org/10.1145/1148170.1148177
Del Vicario, M. and Vivaldo, G. and Bessi, A. and Zollo, F. and Scala, A. and Caldarelli, G. and Quattrociocchi, W. (2016). Echo Chambers: Emotional Contagion and Group Polarization on Facebook. Scientific Reports, 6(1), 37825. DOI: https://doi.org/10.1038/srep37825
Harambam, J. and Helberger, N. and Van Hoboken, J. (2018). Democratizing algorithmic news recommenders: how to materialize voice in a technologically saturated media ecosystem. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180088. DOI: https://doi.org/10.1098/rsta.2018.0088
Mortensen, O (2025). How Many Users Does Truth Social Have? Statistics & Facts Pariser, E. (. URL: https://seo.ai/blog/how-many-users-does-truth-social-have (accessed 2025-11-17)
Rassameeroj, I. and Wu, S. F. (2019). Reverse Engineering of Content Delivery Algorithms for Social Media Systems. Spain, 196-203. DOI: https://doi.org/10.1109/SNAMS.2019.8931859
Zarsky, T. Z (2002). Mine your own business: making the case for the implications of the data mining of personal information in the forum of public opinion. Yale Journal of Law and Technology, 5, 1-56. URL: https://yjolt.org/mine-your-own-business-making-case-implications-data-mining-personal-information-forum-public
Datareportal (2025). Digital 2025: Global Overview Report. URL: https://datareportal.com/reports/digital-2025-global-overview-report (accessed 2025-07-07)
Figà Talamanca, G. and Arfini, S (2022). Through the Newsfeed Glass: Rethinking Filter Bubbles and Echo Chambers. Philosophical Technology, 35(1). DOI: https://doi.org/10.1007/s13347-021-00494-z
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Copyright (c) 2025 Elena Kirilova (Author)
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