From Editors to Algorithms

Abstract

Facebook’s News Feed is an emerging, influential force in our personal information flows, especially where news information is concerned. However, as the News Feed’s story selection mechanism starts to supplant traditional editorial story selection, we have no window into its story curation process that is parallel to our extensive knowledge of the news values that drive traditional editorial curation. The sensitive, trade-secret nature of the News Feed and its constant updates and modifications make a traditional, computer science-based examination of this algorithmic giant difficult, if not impossible. This study takes an alternative approach, using a content analysis of Facebook’s own patents, press releases, and Securities and Exchange Commission filings to identify a core set of algorithmic values that drive story selection on the Facebook News Feed. Informed by the principles of material culture analysis, it ranks these values to create a window into Facebook’s curation process, and compares and contrasts Facebook’s story selection values with traditional news values, examining the possible consequences of one set of values supplanting the other. The study finds a set of nine News Feed values that drive story selection: friend relationships, explicitly expressed user interests, prior user engagement, implicitly expressed user preferences, post age, platform priorities, page relationships, negatively expressed preferences, and content quality. It also finds evidence that friend relationships act as an overall influence on all other story selection values.

Keywords:

• algorithmic curation
• algorithms
• content analysis
• critical algorithm studies
• Facebook
• material culture analysis
• news information
• news values

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Corrigendum

Acknowledgements

The author thanks Eileen Emerson for her research assistance, as well as Kerric Harvey, David Karpf, and Emily Thorson of The George Washington University for their guidance and support during the initial research on this paper. The author also thanks the anonymous reviewers, William Marler, and the members of Aaron Shaw’s “Bring Your Own Research” group at Northwestern University for their feedback on the manuscript.