Impact of Customizability Technology on Political Polarization

Block, Hide or Follow—Personal News Curation Practices on Social Media

“…Concerns that news-limiting behavior might be linked to more extreme political attitudes were not substantiated for all countries apart from the US. Even though no statements about the exact influence of personal curatorial practices on the mix of content on social media newsfeeds can be made from this study, the results suggest that curatorial practices might act as a counterpoint to the possible constricting effects of system-driven customization caused by algorithmic filtering (Dylko et al 2018). From a platform perspective, the software features that allow the blocking of contacts or offer the ability to reduce the amount of specific content could not only let users view the platform more favorably and avoid the cutting of ties but also prevent news fatigue or information overload.…”

“…They found a positive influence of following civic and political pages, a potential practice of personal curation. In an experiment with a mock-up online website, Dylko et al (2017Dylko et al ( , 2018 demonstrated that the possibility of user-driven curation (modify the issue or ideology of news content consumed) can increase partisan selective exposure while at the same time it can also weaken the relationship between system-driven customizability (seeing only ideological content according to one's orientation or previous behavior) and partisan selective exposure. Since partisan selective exposure is linked to political polarization (Stroud 2010) and, therefore, not necessarily preferable from a normative democratic standpoint, studying the prevalence and practice of user-driven personal news curation can also help us understand how the dynamics of selective exposure on social media can be minimized.…”

A guideline for understanding and measuring algorithmic governance in everyday life

“…Accompanying these considerations are fears of personalised echo chambers (Sunstein, 2001) or filter bubbles (Pariser, 2011), leading to fragmented, biased perceptions of society (Dylko, 2016). However, recent empirical studies fail to show a coherent picture: there are clear patterns of algorithmically induced, homogenous opinion networks (Bakshy, Messing, & Adamic, 2015;Del Vicario et al, 2016;Dylko et al, 2017), but other studies indicate more opinion diversity despite algorithmic selection and qualify the risk of echo chambers with empirical evidence (Barbera, Jost, Nagler, Tucker, & Bonneau, 2015;Dubois & Blank, 2018;Fletcher & Nielsen, 2017;Heatherly, Lu, & Lee, 2017;Helberger, Bodo, Zuiderveen Borgesius, Irion, & Bastian, 2017;Zuiderveen Borgesius et al, 2016).…”

A Conceptual Tool to Eliminate Filter Bubbles in Social Networks

“…Previous literature has not found a significant relationship between exposure to an opposing political view and a change in people's political opinion (Bail et al, 2018). However, there are many studies that investigated the impact of filter bubbles on a commitment to a populist cause (Postill, 2018) and avoidance of cross-referencing (Van den Bulck & Moe, 2018), which creates a risk to the diversity of opinions and well-functioning democracy as a result (Bozdag & van den Hoven, 2015;Dylko et al, 2018).…”