ArticleAnnals of the New York Academy of Sciences2025
Designing social media content recommendation algorithms for societal good.
Article in Annals of the New York Academy of Sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- A cross sectional content analysis evaluates chemotherapy health information quality and reliability on TikTok and Bilibili.Scientific reports · 2026Article
- The impact of social media use intensity on college students' self-esteem: the mediating roles of online group identity and online selective exposure.Frontiers in psychology · 2026Article
- Quality and reliability of myelodysplastic syndromes-related health information on Chinese short-video platforms: a multi-platform cross-sectional analysis.Frontiers in digital health · 2026Article
- Designing social media content recommendation algorithms for societal good.Annals of the New York Academy of Sciences · 2025Article
- Cross-platform assessment of short-form video quality on the gut-liver axis: informational integrity and engagement disparity.Frontiers in cellular and infection microbiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
In the face of mounting evidence for a relationship between social media platforms and detrimental societal outcomes such as polarization, the erosion of trust in institutions, and the spread of misinformation, this perspective argues for the design of alternative content recommendation algorithms that serve the societal good and a lively democratic discourse. We propose to approach the design of content recommendation algorithms through the lens of fostering a healthy civic discourse, which serves to identify dimensions of relevance to guide the development of content recommendation algorithms. This approach lends alternative content recommendation algorithms legitimacy by being rooted in the EU's novel Digital Services Act and by aligning content recommendation with democratic values. We explore the trade-off between interventions in content recommendation and freedom of expression and propose a research agenda that uses approaches from multistakeholder metric construction and scenario-based risk assessment to find situation-dependent just balances between individual rights and societal outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.