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Prioritizing Original News on Facebook
Open accessAbstract
This work outlines how we prioritize original news, a critical indicator of news quality. By examining the landscape and lifecycle of news posts on our social media platform, we identify challenges of building and deploying an originality score. We pursue an approach based on normalized PageRank values and three-step clustering, and refresh the score on an hourly basis to capture the dynamics of online news. We describe a near real-time system architecture, evaluate our methodology, and deploy it to production. Our empirical results validate individual components and show that prioritizing original news increases user engagement with news and improves proprietary cumulative metrics.
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Engagement, user satisfaction, and news originality are key signals used by the Facebook feed algorithm to determine how to rank content for individual users.
“Engagement, user satisfac-tion, and news originality are key signals used by the Facebook feed algorithm to determine how to rank content for individual users [more publicly available information is available in (20, 39, 40, 41)] (SM section S1.1).”
From How do social media feed algorithms affect attitudes and behavior in an election campaign? (2023) · cites this paper as [39]
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References 25 · Cited by 3
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Metadata
- Authors
- Ni, X., Bu, S., Adams, L., & Markov, I. L.
- Affiliations
- Meta (United States) (US)
- Venue
- Proceedings of the 30th ACM International Conference on Information & Knowledge Management
- Publisher
- ACM
- Published
- 2021-10-26
- Pages
- 4046-4054
- ISSN
- 2155-0751 (ISSN-L)
- OpenAlex
- W3208111416
- Cited by
- 3
- References
- 25
- Citations by year
- 2025: 1 · 2023: 2
- FWCI
- 0.41
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- news quality, Facebook, PageRank, user engagement, social media news
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