Facebook, Inc.

Meta Platforms, Inc.

Danilo Torres De Sa Resende

David Gross-Baser

Janaka Liyanage

Ami Vora

Srikant Ramakrishna Ayyar

Ajoy Joseph Frank

Wenrui Zhao

Louis Eisenberg

Thomas Frederick Dimson

Amaç Herdagdelen

Eric Michel Giovanola

Bogdan State

Ehud Weinsberg

Bai Xiao

Eric Michael Giovanola

Systems, methods, and non-transitory computer-readable media can acquire a set of labels associated with a set of content items. Each label in the set of labels can be associated with at least one content item in the set of content items. It can be determined that at least two labels, out of the set of labels, are related. The at least two labels can be determined to be related based on at least one of a co-occurrence metric associated with the at least two labels or a topic similarity metric associated with the at least two labels. One label can be selected, out of the at least two labels, as being representative of the at least two labels.

Systems and methods for identifying and grouping related content labels

A social networking system includes pages created by users for providing content related to topics of interest. An interaction engine captures data and maintains analytical information about how users interact with pages and posts. When a user takes an action such as liking a page or a post, the interaction engine updates the page's activity history to reflect the activity and information about the user who took the action. The interaction engine also measures the reach of pages and posts, by determining how and from where each user viewed the page and its contents. The interaction engine further tracks the community's interest in pages and posts by determining how many unique users create stories that include a page or its content. An insights module uses the data obtained by the interaction engine to synthesize graphical reports about page interactions and present the reports to page administrators.

Measuring page viewership in a social network

An online system allows creators and owners of content items presented within the system to view real-time metrics related to the content items. The online system stores data describing user interactions with content items in one or more activity logs and processes the activity logs in a distributed manner activity to identify user interactions with content items. For example, activity logs are divided into several portions that are processed in parallel using aggregation operators to generate user interaction aggregations. An aggregation includes one or more user interactions associated with the same content item and sharing certain criteria. From the user interaction aggregations, the online system determines one or more requested metrics, and may combine user interaction aggregations related to different content items when computing metrics.

Generating metrics for content items presented in an online system

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San Francisco, CA, United States of America

Danilo Torres De Sa Resende