Project description
Understanding the impact of link recommendations on social dynamics
Understanding the influence of algorithms on human behaviour in online social media is vital. Link-recommendation algorithms, which suggest new connections to users, are pervasive and fundamentally shape how new connections are forged and the information users encounter. Effectively governing online social networks necessitates a deep understanding of these algorithms’ effects and adapting them for long-term advantages. In this context, the ERC-funded RE-LINK project aims to develop novel computational models to grasp the impact of link recommendations on social dynamics. The project will devise a new link-recommender paradigm aimed at fostering positive social behaviours while balancing short-term performance with long-term societal benefits. Evaluation of the algorithms will be conducted through large-scale multi-agent simulations, online experiments and real-world data analysis.
Objective
This proposal aims to develop, for the first time, new computational models to systematically evaluate 1) the long-term societal impacts of link-recommendation algorithms in online social networks and 2) design a new paradigm of link-recommenders that incentivize cooperation, collective action, and misinformation control.
It is urgent to understand how algorithms used in online social media impact human behavioural dynamics given the widespread use of social media platforms and the evidence that they contribute to exacerbate radicalization, misinformation, and incite hate. This is a challenging endeavour. Online platforms are nowadays complex ecosystems where millions of humans influence each other while co-existing with AI algorithms. In this context, link-recommendation algorithms, used to recommend new connections to users, are ubiquitous. Such algorithms fundamentally affect how new connections are formed and the information users are exposed to. Governing online social networks requires understanding the impact of link-recommenders and how to adapt them to ensure long-term benefits.
With RE-LINK, I aim to develop a new class of models to understand the impact of link-recommendations on social dynamics and, in turn, design a new paradigm of algorithms that balance short-term performance and long-term societal benefits. This will be achieved by developing agent-based models where the evolution of behaviours occurs over adaptive networks whose growth, in turn, follows the heuristics used by link-recommenders. I will resort to evolutionary game theory and stochastic population dynamics to formally study the stability of behaviours in this setting. I will use the modelling results to design new link-recommenders that contribute to stabilize positive social behaviours such as cooperation, collective action, and misinformation debunking. The developed algorithms will be evaluated with large-scale multi-agent simulations, online experiments, and real-world data.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencesbiological sciencesevolutionary biology
- natural sciencescomputer and information sciencescomputational science
- natural sciencesmathematicsapplied mathematicsgame theory
- natural sciencesmathematicspure mathematicsdiscrete mathematicsgraph theory
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Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
1012WX Amsterdam
Netherlands