Overall, research indicates that the risk of getting stuck in a filter bubble on intermediaries such as Google News, Apple News, Facebook, or Twitter is low and often exaggerated

News recommender systems: a programmatic research review. Eliza Mitova et al. Annals of the International Communication Association, Nov 11 2022. https://doi.org/10.1080/23808985.2022.2142149

Abstract: News recommender systems (NRS) are becoming a ubiquitous part of the digital media landscape. Particularly in the realm of political news, the adoption of NRS can significantly impact journalistic distribution, in turn affecting journalistic work practices and news consumption. Thus, NRS touch both the supply and demand of political news. In recent years, there has been a strong increase in research on NRS. Yet, the field remains dispersed across supply and demand research perspectives. Therefore, the contribution of this programmatic research review is threefold. First, we conduct a scoping study to review scholarly work on the journalistic supply and user demand sides. Second, we identify underexplored areas. Finally, we advance five recommendations for future research from a political communication perspective.

Keywords: News recommender systemsalgorithmsdigital journalismnews personalisation

Overall, research indicates that the risk of getting stuck in such a bubble on intermediaries such as Google News (e.g. Evans et al., 2022; Haim et al., 2018; Nechushtai & Lewis, 2019), Apple News (e.g. Bandy & Diakopoulos, 2020), Facebook (e.g. Bakshy et al., 2015; Beam et al., 2018; Bechmann & Nielbo, 2018; Moeller et al., 2016; Papa & Photiadis, 2021; but see Levy, 2021 for divergent findings), or Twitter (e.g. Bandy & Diakopoulos, 2021; Chen et al., 2021; but see Jürgens & Stark, 2022 for divergent findings) is low and often exaggerated