SubsidieMeestersResponsible Link-Recommendations in Dynamic Environments
This project aims to create computational models to assess and redesign link-recommendation algorithms for online social networks to promote cooperation and mitigate misinformation.
Projectdetails
Introduction
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.
Importance of Understanding Algorithms
It is urgent to understand how algorithms used in online social media impact human behavioral 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 endeavor. 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.
Project Goals
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.
Methodology
This will be achieved by developing agent-based models where the evolution of behaviors 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 behaviors in this setting.
- I will use the modeling results to design new link-recommenders that contribute to stabilize positive social behaviors such as:
- Cooperation
- Collective action
- Misinformation debunking
Evaluation
The developed algorithms will be evaluated with large-scale multi-agent simulations, online experiments, and real-world data.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 1.500.000 |
| Totale projectbegroting | € 1.500.000 |
Tijdlijn
| Startdatum | 1-3-2024 |
| Einddatum | 28-2-2029 |
| Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- UNIVERSITEIT VAN AMSTERDAMpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
| Project | Regeling | Bedrag | Jaar | Actie |
|---|---|---|---|---|
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Designing Social Media Recommendation Algorithms for Societal Good
The project aims to enhance social media algorithms by integrating civic discourse values to reduce risks to social cohesion while balancing freedom of expression through participatory design and risk assessment.
Comprehending Human Action using Social Networks
This project explores how social networks influence the behavior of scientists, politicians, and citizens through empirical analyses and network-based identification strategies.
Collective Adaptation
This project aims to develop a scientific paradigm for studying collective adaptation by integrating cognitive and social processes through computational models and empirical data to address societal challenges.
Computational Mechanisms of Social Media Use in Youth
This project aims to develop computational models and analyze social media trace data to understand youth engagement and its neurocognitive impacts, enhancing future research frameworks.
Society-Aware Machine Learning: The paradigm shift demanded by society to trust machine learning.
The project aims to develop society-aware machine learning algorithms through collaborative design, balancing the interests of owners, consumers, and regulators to foster trust and ethical use.