Skewed perspectives: examining the influence of engagement maximization on content diversity in social media feeds

This article investigates the information landscape shaped by curation algorithms that seek to maximize user engagement. Leveraging unique behavioral data, we trained machine learning models to predict user engagement with tweets. Our study reveals how the pursuit of engagement maximization skews co...

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Veröffentlicht in:Journal of computational social science Jg. 7; H. 1; S. 721 - 739
1. Verfasser: Bouchaud, Paul
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 01.04.2024
Schlagworte:
Big Data/Analytics
Complex Systems
Computational Linguistics
Research Article
Simulation and Modeling
Social Media
Social Sciences
Algorithm audit
User models
Engagement maximization
Social media
Diversity
ISSN:2432-2717, 2432-2725
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This article investigates the information landscape shaped by curation algorithms that seek to maximize user engagement. Leveraging unique behavioral data, we trained machine learning models to predict user engagement with tweets. Our study reveals how the pursuit of engagement maximization skews content visibility, favoring posts similar to previously engaged content while downplaying alternative perspectives. The empirical grounding of our work provides a basis for evidence-based policies aimed at fostering responsible social media platforms.
ISSN:2432-2717
2432-2725
DOI:10.1007/s42001-024-00255-w