Decoding YouTube's Recommendation System: A Comparative Study of Metadata and GPT-4 Extracted Narratives

Author:

Gurung Mayor Inna1ORCID,Bhuiyan Md Monoarul Islam2ORCID,Al-Taweel Ahmed1ORCID,Agarwal Nitin1ORCID

Affiliation:

1. COSMOS Research Center, UA Little Rock, Little Rock, AR, USA

2. COSMOS Research Center, UA Little Rock, Little Rock, USA

Funder

U.S. Office of the Under Secretary of Defense for Research and Engineering

U.S. Army Research Office

U.S. National Science Foundation

U.S. Air Force Research Laboratory, U.S. Defense Advanced Research Projects Agency

Arkansas Research Alliance, the Jerry L. Maulden/Entergy Endowment at the University of Arkansas at Little Rock, and the Australian Department of Defense Strategic Policy Grants Program (SPGP)

U.S. Office of Naval Research

Publisher

ACM

Reference20 articles.

1. X.Wang C. Li M. Najork A. Agarwal I. Zaitsev and T. Joachims. 2019. Estimating Position Bias without Intrusive Interventions.

2. B. M. Stewart A. J. B. Chaney and B. E. Engelhardt. 2018. How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. NewYork USA.

3. S. L. Johnson B. Kitchens and P. Gray. 2020. Understanding Echo Chambers and Filter Bubbles: The Impact of Social Media on Diversification and Partisan Shifts in News Consumption.

4. E.Amorim B.Santana, R. Campos and Silvano A.Jorge. 2023. A survey on narrative extraction from textual data.

5. Adopting Parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment

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