Abstract: The present disclosure relates to systems and methods for increasing messaging activity in a messaging system. Using the interactions of users with each other and/or with the messaging system, the disclosed systems and methods can predict how likely a pairing of two or more users are to engage in a highly active messaging thread. Based on this prediction, the disclosed methods and systems can, for example, more effectively organize contact lists and conduct promotional efforts associated with messaging features.

Abstract: The present disclosure relates to systems and methods for increasing messaging activity in a messaging system. Using the interactions of users with each other and/or with the messaging system, the disclosed systems and methods can predict how likely a pairing of two or more users are to engage in a highly active messaging thread. Based on this prediction, the disclosed methods and systems can, for example, more effectively organize contact lists and conduct promotional efforts associated with messaging features.

Abstract: The present disclosure relates to systems and methods for increasing messaging activity in a messaging system. Using the interactions of users with each other and/or with the messaging system, the disclosed systems and methods can predict how likely a pairing of two or more users are to engage in a highly active messaging thread. Based on this prediction, the disclosed methods and systems can, for example, more effectively organize contact lists and conduct promotional efforts associated with messaging features.

Abstract: The present disclosure relates to systems and methods for increasing messaging activity in a messaging system. Using the interactions of users with each other and/or with the messaging system, the disclosed systems and methods can predict how likely a pairing of two or more users are to engage in a highly active messaging thread. Based on this prediction, the disclosed methods and systems can, for example, more effectively organize contact lists and conduct promotional efforts associated with messaging features.

Abstract: Systems, methods, and non-transitory computer-readable media can receive an image. One or more concepts depicted in the image are identified based on machine learning techniques. The one or more concepts are filtered based on filtering criteria to identify one or more selected concepts. An image description is generated comprising the one or more selected concepts.

Abstract: The present disclosure relates to systems and methods for increasing messaging activity in a messaging system. Using the interactions of users with each other and/or with the messaging system, the disclosed systems and methods can predict how likely a pairing of two or more users are to engage in a highly active messaging thread. Based on this prediction, the disclosed methods and systems can, for example, more effectively organize contact lists and conduct promotional efforts associated with messaging features.

Abstract: Methods, systems, computer-readable media, and apparatuses for using, by one or more computer devices, engagement actions by users of a social network as behavioral signals for evaluating the quality of a content item shared by those users. Once the quality of a content item has been evaluated in this manner, an action can be performed on that content item (e.g., changing the visibility of that content item within the social network).

Abstract: Some embodiments include a method of detecting and analyzing virally propagating subject matter in a social networking system. The method includes processing user activities in the social networking system through a relevancy filter to identify a subset of user activities that are relevant to a viral propagation study. The social networking system can construct, in response to selecting a user activity as a graph exploration seed, a user activity cascade by exploring the social graph in the social networking system, starting from a social network node corresponding to the user activity. The user activity cascade can comprise social network nodes found during the graph exploration. The social networking system can determine that the user activity cascade is virally propagating based at least upon a total size of the user activity cascade.

Abstract: In one embodiment, a method includes receiving unstructured text from a user of a social-networking system, determining whether the unstructured text includes a request for a recommendation, identifying one or more first entity names in the unstructured text, generating a structured query based upon the one or more first entity names, identifying, in the social graph, one or more second entity names corresponding to the structured query, and presenting the one or more second entity names and the unstructured text in a social context of the user. The unstructured text may include text of a post or message generated by the user on a social-networking system. A score may be generated based on the unstructured text to determine whether the text includes a request for recommendation using a machine-learning model based on comparison of the unstructured text to the one or more predetermined words associated with requests for recommendation.

Abstract: Systems, methods, and non-transitory computer-readable media can receive an image. One or more concepts depicted in the image are identified based on machine learning techniques. The one or more concepts are filtered based on filtering criteria to identify one or more selected concepts. An image description is generated comprising the one or more selected concepts.

Abstract: Systems, methods, and non-transitory computer-readable media can receive a plurality of comments to a posted content item. Each of the plurality of comments is associated with at least one category of a plurality of categories based on a machine learning model. A first comment of the plurality of comments is selected for inclusion in a comment sample to be presented in a graphical user interface based on the first comment being associated with a first category of the plurality of categories. A second comment of the plurality of comments is selected for inclusion in the comment sample based on the second comment being associated with a second category of the plurality of categories.

Abstract: The present disclosure relates to systems and methods for increasing messaging activity in a messaging system. Using the interactions of users with each other and/or with the messaging system, the disclosed systems and methods can predict how likely a pairing of two or more users are to engage in a highly active messaging thread. Based on this prediction, the disclosed methods and systems can, for example, more effectively organize contact lists and conduct promotional efforts associated with messaging features.

Abstract: In one embodiment, a method includes receiving unstructured text from a user of a social-networking system, determining whether the unstructured text includes a request for a recommendation, identifying one or more first entity names in the unstructured text, generating a structured query based upon the one or more first entity names, identifying, in the social graph, one or more second entity names corresponding to the structured query, and presenting the one or more second entity names and the unstructured text in a social context of the user. The unstructured text may include text of a post or message generated by the user on a social-networking system. A score may be generated based on the unstructured text to determine whether the text includes a request for recommendation using a machine-learning model based on comparison of the unstructured text to the one or more predetermined words associated with requests for recommendation.

Abstract: Embodiments of the present invention are directed to a method for information management. In one embodiment, the method includes providing an organizational directory comprising hierarchical information regarding an organization and receiving relationship data including an association of a first person to a second person. The method also includes querying the organizational directory and the relationship data to identify a third person, wherein the third person is a member of the organization and identifying the third person comprises determining a path including the third person in an association network.