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

Paul Bouchaud ()
Additional contact information
Paul Bouchaud: EHESS/CAMS

Journal of Computational Social Science, 2024, vol. 7, issue 1, No 28, 739 pages

Abstract: Abstract 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.

Keywords: Algorithm audit; Social media; Engagement maximization; Diversity; User models (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s42001-024-00255-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcsosc:v:7:y:2024:i:1:d:10.1007_s42001-024-00255-w

Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001

DOI: 10.1007/s42001-024-00255-w

Access Statistics for this article

Journal of Computational Social Science is currently edited by Takashi Kamihigashi

More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().