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Uncovering the Interaction Equation: Quantifying the Effect of User Interactions on Social Media Homepage Recommendations

2024/07/09 by Hussam Habib, Ryan Stoldt, Habib, Hussam +7
Decision Sciences · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Computers and Society (cs.CY) #Digital Marketing and Social Media #FOS: Computer and information sciences #Social and Information Networks (cs.SI) #Technology Adoption and User Behaviour

paper · pdf · doi:10.48550/arxiv.2407.07227

openalex publication_date 2024/07/09 · openalex created_date 2024/07/13 · openalex updated_date 2026/07/28

Abstract

Social media platforms depend on algorithms to select, curate, and deliver content personalized for their users. These algorithms leverage users' past interactions and extensive content libraries to retrieve and rank content that personalizes experiences and boosts engagement. Among various modalities through which this algorithmically curated content may be delivered, the homepage feed is the most prominent. This paper presents a comprehensive study of how prior user interactions influence the content presented on users' homepage feeds across three major platforms: YouTube, Reddit, and X (formerly Twitter). We use a series of carefully designed experiments to gather data capable of uncovering the influence of specific user interactions on homepage content. This study provides insights into the behaviors of the content curation algorithms used by each platform, how they respond to user interactions, and also uncovers evidence of deprioritization of specific topics.

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