Uncovering the Interaction Equation: Quantifying the Effect of User Interactions on Social Media Homepage Recommendations
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910521231409152 |
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| author | Habib, Hussam Stoldt, Ryan Maragh-Lloyd, Raven Ekdale, Brian Nithyanand, Rishab |
| author_facet | Habib, Hussam Stoldt, Ryan Maragh-Lloyd, Raven Ekdale, Brian Nithyanand, Rishab |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07227 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uncovering the Interaction Equation: Quantifying the Effect of User Interactions on Social Media Homepage Recommendations Habib, Hussam Stoldt, Ryan Maragh-Lloyd, Raven Ekdale, Brian Nithyanand, Rishab Social and Information Networks Computers and Society 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. |
| title | Uncovering the Interaction Equation: Quantifying the Effect of User Interactions on Social Media Homepage Recommendations |
| topic | Social and Information Networks Computers and Society |
| url | https://arxiv.org/abs/2407.07227 |