Abstract:
In particular embodiments, a social networking system queries a social graph of the social-networking system for social content associated with video content provided to one or more users of the social-networking system and accesses privacy settings associated with each of the one or more users. The privacy settings indicate whether a particular user has authorized the social-networking system to share social content associated with the particular user with one or more third parties. The social networking system provides the social content associated with video content provided to the one or more users to a particular third party if the privacy settings of the one or more users indicate that the one or more users have authorized the social-networking system to share the social content with one or more third parties.
Abstract:
Users of a social networking system are assigned to households using prediction models that rely, in part, on user profile information and social graph data. Information about users may be received by a social networking system through various channels (e.g., declared/profile information, user history, IP addresses, Global Positioning System (GPS) data from check-in events and/or continuously provided by mobile devices, external household information, and/or social information). Scoring models may use statistical analysis of the received user information to predict household membership for users. User attributes, such as previous names, date of birth, social graph data, locations, life events, and check-ins, may be factors in generating confidence scores of predicted household memberships. Weighted scoring models may use machine learning methods for measuring the accuracy of the household membership prediction. The social networking system may use a machine learning algorithm to analyze user information to determine confidence scores for matching potential households.
Abstract:
A social networking system identifies communications about an object associated with a brand owner. For each communication, the social networking system identifies users who were generated the communication, users who were exposed to the communication, and users who were not exposed to the communication. The social networking system measures the impact of the communications on the behavior and/or sentiment of the users towards the brand owner. For example, the social networking system presents users with surveys after presentation of a communication about an object associated with a brand owner and determines the impact of the communication from the responses to the survey. The impact of the communications may then be reported to the brand owner.
Abstract:
Embodiments of the invention combine information from different data sets, such as social networks, advertising networks, and/or panels, each data set comprising statistics about past viewership of content (e.g., advertisements). The result of the combination is a model that, when applied to statistics about viewing of particular content, produces viewing statistics about the particular content that are more accurate than the data of any given one of the different data sets when taken in isolation.
Abstract:
Users of a social networking system are assigned to households using prediction models that rely, in part, on user profile information and social graph data. Information about users may be received by a social networking system through various channels (e.g., declared/profile information, user history, IP addresses, Global Positioning System (GPS) data from check-in events and/or continuously provided by mobile devices, external household information, and/or social information). Scoring models may use statistical analysis of the received user information to predict household membership for users. User attributes, such as previous names, date of birth, social graph data, locations, life events, and check-ins, may be factors in generating confidence scores of predicted household memberships. Weighted scoring models may use machine learning methods for measuring the accuracy of the household membership prediction. The social networking system may use a machine learning algorithm to analyze user information to determine confidence scores for matching potential households.
Abstract:
In particular embodiments, a social networking system queries a social graph of the social-networking system for social content associated with video content provided to one or more users of the social-networking system and accesses privacy settings associated with each of the one or more users. The privacy settings indicate whether a particular user has authorized the social-networking system to share social content associated with the particular user with one or more third parties. The social networking system provides the social content associated with video content provided to the one or more users to a particular third party if the privacy settings of the one or more users indicate that the one or more users have authorized the social-networking system to share the social content with one or more third parties.
Abstract:
A social networking system allows a user to insert media information into content posted by the user, where the media information identifies a media item that the user is consuming while composing the posted content. When a user of a social networking system composes content via a composer interface, the user may select an option on the composer interface to record audio using a microphone on the user's device. A media item is identified from the recorded audio and information about the identified media item is added to the user's posted content. The system may also update information about the identified media item and the composing user.
Abstract:
A social networking system generates metrics for one or more advertisements based on client device ownership. Social networking system users are identified as owners of client devices. For example, a social networking system user is identified as owning a client device if the user's user account was accessed using a native software application or a web browsing application associated with the client device. Exposures to one or more advertisements by the identified owners are determined and used to generate advertising metrics with respect to the client devices owned by the owners. The metrics may be segmented or organized based on various client device types.