Draw me Science

Chavalarias, David; Lobbé, Quentin; Delanoë, Alexandre

doi:10.1007/s11192-021-04186-5

Cited by 11 publications

(2 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This effervescence of models can also be seen in the reconstruction of the evolution of this field with phylomemies (Figure 2), i.e. the year by year extraction of sub-domains and reconstruction of their lineage (Chavalarias et al 2021). The many ramifications of this phylomemy stemming from the continuous introduction of new concepts and new combinations of concepts, underline the vitality of this field of research.…”

Section: 6mentioning

confidence: 88%

“…In particular, we demonstrated that the self-learning recommender systems that seek to solely maximize users' engagement based on measures of user or content popularity necessarily lead to an overexposure of users to negative content, a phenomenon called algorithmic negativity bias (Chavalarias 2022) and to an ideological fragmentation and polarization of the online opinion landscape. We believe that these results may constitute evidence of a systemic risk within the meaning of Article 26 of the Digital Services Act 9 associated with engagement-maximizing business models on VLOPs 10 ; and could consequently contribute to evidence-based regulation under the Digital Services Act or other international regulatory frameworks.…”

Section: 6mentioning

confidence: 99%

See 1 more Smart Citation

Can a Single Line of Code Change Society? The Systemic Risks of Optimizing Engagement in Recommender Systems on Global Information Flow, Opinion Dynamics and Social Structures

Chavalarias,

Bouchaud,

Panahi

2024

JASSS

View full text Add to dashboard Cite

As the last few years have seen an increase in both online hostility and polarization, we need to move beyond the fact-checking reflex or the praise for better moderation on social networking sites (SNS) and investigate their impact on social structures and social cohesion. In particular, the role of recommender systems deployed by Very Large Online Platforms (VLOPs) such as Facebook or Twitter has been overlooked. This paper draws on the literature on cognitive science, digital media, and opinion dynamics to propose a faithful replica of the entanglement between recommender systems, opinion dynamics and users' cognitive bias on SNSs like Twitter that is calibrated over a large scale longitudinal database of tweets from political activists. This model makes it possible to compare the consequences of various recommendation algorithms on the social fabric, to quantify their interaction with some major cognitive bias. In particular, we demonstrate that the recommender systems that seek to solely maximize users' engagement necessarily lead to a polarization of the opinion landscape, to a concentration of social power in the hands of the most toxic users and to an overexposure of users to negative content (up to 300% for some of them), a phenomenon called algorithmic negativity bias. Toxic users are more than twice as numerous in the top 1% of the most influential users than in the overall population. Overall, our results highlight the systemic risks generated by certain implementations of recommender systems and the urgent need to comprehensively identify implementations of recommender systems harmful to individuals and society. This is a necessary step in setting up future regulations for systemic SNSs, such as the European Digital Services Act.

show abstract

Section: 6mentioning

confidence: 88%

Section: 6mentioning

confidence: 99%

Can a Single Line of Code Change Society? The Systemic Risks of Optimizing Engagement in Recommender Systems on Global Information Flow, Opinion Dynamics and Social Structures

Chavalarias,

Bouchaud,

Panahi

2024

JASSS

View full text Add to dashboard Cite

show abstract

Netzwerke in der quantitativen Wissenschaftsforschung

Scharnhorst,

Havemann

2023

Netzwerkforschung

View full text Add to dashboard Cite

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the KNAW public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain.• You may freely distribute the URL identifying the publication in the KNAW public portal. Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

show abstract