Serendipity by Design? How to Turn from Diversity Exposure to Diversity Experience to Face Filter Bubbles in Social Media

Recommender systems and their ethical challenges

“…A much-discussed effect of some recommender systems is their transformative impact on society. In particular, news recommender systems and social media filters, by nature of their design, run the risk of insulating users from exposure to different viewpoints, creating self-reinforcing biases and "filter bubbles" that are damaging to the normal functioning of public debate, group deliberation, and democratic institutions more generally (Bozdag 2013;Bozdag and van den Hoven, 2015;Harambam et al 2018;Helberger et al 2016;Koene et al 2015;Reviglio 2017;Zook et al 2017). This feature of recommender systems can have negative effects on social utility.…”

“…The literature on the topic proposes a range of approaches to increase the diversity of recommendations. A point noted by several authors is that news recommendation systems, in particular, must reach a trade-off between the expected relevance to the user and diversity when generating personalised recommendations based on pre-specified user preferences or behavioural data (Helberger et al 2016;Reviglio 2017). In this respect, Bozdag and van den Hoven (2015) argue that the design of algorithmic tools to combat informational segregation should be more sensitive to the democratic norms that are implicitly built into these tools.…”

Three contextual dimensions of information on social media: lessons learned from the COVID-19 infodemic

“…At least in regular, day to day situations, users turn to social media platforms to relate, to communicate and to be entertained (Fuchs 2014). The weak epistemic context of social media is ruled by serendipity (Reviglio 2017), meaning that many users get to be informed by accident.…”

Recommender Systems and their Ethical Challenges

“…In particular, news recommender systems and social media filters, by nature of their design, run the risk of insulating users from exposure to different viewpoints, creating self-reinforcing biases and "filter bubbles" that are damaging to the normal functioning of public debate, group deliberation, and democratic institutions more generally (Bozdag, 2013;Bozdag & van den Hoven, 2015;Harambam, Helberger, & van Hoboken, 2018;Helberger, Karppinen, & D'acunto, 2016;Koene et al, 2015;Reviglio, 2017;Zook et al, 2017). A closely related issue is protecting these systems from manipulation by (sometimes even small but) especially active groups of users, whose interactions with the system can generate intense positive feedback, driving up the system's rate of recommendations for specific items (Chakraborty, Patro, Ganguly, Gummadi, & Loiseau, 2019).…”

“…The literature on the topic proposes a range of approaches to increase the diversity of recommendations. A point noted by several authors is that news recommendation systems, in particular, must reach a trade-off between the expected relevance to the user and diversity when generating personalised recommendations based on pre-specified user preferences or behavioural data (Helberger et al, 2016;Reviglio, 2017). In this respect, (Bozdag & van den Hoven, 2015) argue that the design of algorithmic tools to combat informational segregation should be more sensitive to the democratic norms that are implicitly built into these tools.…”