Can Filter Bubbles Protect Information Freedom? Discussions of Algorithmic News Recommenders in Eastern Europe

Makhortykh, Mykola; Wijermars, Mariëlle (2021). Can Filter Bubbles Protect Information Freedom? Discussions of Algorithmic News Recommenders in Eastern Europe. Digital Journalism, pp. 1-25. Taylor & Francis 10.1080/21670811.2021.1970601

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The increasing use of recommender systems to provide personalized news delivery influences media systems worldwide. Using different data sources to predict what content will be interesting for specific readers, recommender systems can better accommodate individual information needs, but also raise concerns about potential audience fragmentation. However, current assessments of the effects of news personalization are predominantly based on observations from Western democracies. This Western-centric approach raises concerns about these assessments’ applicability to other contexts, in particular non-democratic ones, and brings to question the influence of prevalent Western conceptualisations of news personalization (e.g., filter bubbles) on attitudes towards it in non-Western countries. To address this gap, we scrutinize discussions of the promises and threats of news personalization in countries characterized by limited press freedom: Belarus, Russia and Ukraine. Using document analysis, we examine how three categories of actors—academics, journalists and IT specialists—discuss news personalization and the ways it can affect the public sphere. Through our analysis we uncover how Western conceptualisations of news personalization interact with discussions about it in non-democratic media systems and scrutinize whether existing concerns about personalization are applicable to non-Western contexts.

Item Type:

Journal Article (Original Article)

Division/Institute:

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

UniBE Contributor:

Makhortykh, Mykola

Subjects:

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

ISSN:

2167-082X

Publisher:

Taylor & Francis

Language:

English

Submitter:

Mykola Makhortykh

Date Deposited:

04 Oct 2021 12:42

Last Modified:

04 Oct 2021 12:42

Publisher DOI:

10.1080/21670811.2021.1970601

Uncontrolled Keywords:

algorithms, news personalization, filter bubbles, online news, Russia, Ukraine, Belarus, post-Soviet, digital media

BORIS DOI:

10.48350/159625

URI:

https://boris.unibe.ch/id/eprint/159625

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