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Designing social media content recommendation algorithms for societal good
Jana Lasser1,2, Nikolaus Poechhacker1,3
1IDea_Lab, University of Graz, Graz, Austria.
Annals of the New York Academy of Sciences
|May 5, 2025
Summary
This study proposes designing social media algorithms to promote healthy civic discourse and democratic values, balancing individual expression with societal well-being. It offers a new approach to content recommendation systems.
Area of Science:
- Social Science
- Computer Science
- Law
Background:
- Social media platforms are linked to societal harms like polarization and misinformation.
- Current content recommendation algorithms may exacerbate these issues.
- There is a need for algorithms that prioritize societal good and democratic discourse.
Purpose of the Study:
- To advocate for alternative content recommendation algorithms designed for societal benefit.
- To explore how fostering healthy civic discourse can guide algorithm development.
- To align content recommendation with democratic values and the EU Digital Services Act.
Main Methods:
- Adopting a civic discourse lens to identify relevance dimensions for algorithms.
- Examining the trade-offs between content recommendation interventions and freedom of expression.
- Proposing a research agenda utilizing multistakeholder metric construction and risk assessment.
Main Results:
- Identified key dimensions for designing algorithms that support civic discourse.
- Explored the complex balance between free expression and societal outcomes.
- Outlined a framework for achieving situation-dependent equilibrium between rights and societal impact.
Conclusions:
- Alternative content recommendation algorithms can be designed to foster a healthier public sphere.
- Aligning algorithms with democratic values and regulatory frameworks (e.g., Digital Services Act) is crucial.
- Further research is needed to balance individual freedoms with collective societal well-being in digital spaces.
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