Abstract: In one embodiment, a method includes receiving information associated with a project for creation of digital content and generating an object including the received information, where the object is stored by a social-networking system. The method also includes causing a data item associated with a blockchain network to be generated and deployed to the blockchain network, where the data item is generated based on the information associated with the project that is included in the object, is associated with a type of token recorded in a blockchain associated with the blockchain network, and includes instructions executable according to a protocol associated with the blockchain network. The method further includes collecting information associated with the data item from the blockchain and updating the object based on the collected information.

Abstract: Systems, methods, and non-transitory computer-readable media can determine respective engagement scores for one or more users based at least in part on their engagement with a plurality of content items associated with an entity in a content provider system. At least one user who is a top fan of the entity can be determined based at least in part on the determined engagement scores. The user can be associated with a visual badge in the content provider system to identify the user as a top fan of the entity, wherein the visual badge is made visible by the content provider system to at least the entity.

Abstract: Leads may be generated for content sponsors based on expressions of interest by users, the users being users of an online system. These leads typically require time or expense from a sponsor to follow up on the lead. The online system scores leads for sponsors to prioritize responding to the generated leads. The scores may include a purchase capacity score and a purchase intent score. The purchase capacity score estimates a user's ability to spend an amount specified by the sponsor for the product, and the purchase intent score estimates a user's likelihood to convert or be interested in the sponsor's product. The scores may be combined to provide a single lead quality score for a user that obscures the source of the online system's prediction to the sponsor.

Abstract: An online system communicates a lead generation message to a client device associated with a user. The lead generation message includes a selectable option authorizing a third-party entity to open a channel of communication with the user. If the online system receives from the client device an interaction with the selectable option, the online system sends a notification to the third-party entity indicating that the user associated with the client device interacted with the lead generation content item. The third-party entity may then send a request to the online system to send a message to the user via a messaging system controlled by the online system. The online system thus enables the third-party entity to communicate with the user via the messaging system. The online system then uses information about the communication to train a model to optimize the selection of lead generation messages to users.

Abstract: An online system generates a hierarchical taxonomy including multiple levels arranged in order of increasing specificity, in which each level includes one or more topics with which a content item maintained in the online system may be associated. Based on information associated with presentations of content items to online system users, which may indicate the users' familiarity with the content items, the online system predicts a likelihood that a particular user is familiar with information associated with one or more content items associated with a topic. Based at least in part on the predicted likelihood, the online system generates a connection between the user and the topic, in which the connection corresponds to a predicted level of knowledge that the user has about the topic. The online system may later retrieve the user's predicted level of knowledge about the topic (e.g., to select content for presentation to the user).

Abstract: An online system displays a first set of content items to a user of a test group and displays a second set of content items to a user of a control group. The online system presents a poll to each user to evaluate the user's recall of the content item associated with the poll. The online system receives a poll response from each user, which is input, along with a set of features associated each user, into a prediction model. The prediction model enables the online system to determine a poll response prediction of a third user based on a set of features associated with the third user. The poll response prediction enables the online system to determine if it would be effective to present the content item to the third user.

Abstract: An online system generates a hierarchical taxonomy including multiple levels arranged in order of increasing specificity, in which each level includes one or more topics with which a content item maintained in the online system may be associated. Based on information associated with presentations of content items to online system users, which may indicate the users' familiarity with the content items, the online system predicts a likelihood that a particular user is familiar with information associated with one or more content items associated with a topic. Based at least in part on the predicted likelihood, the online system generates a connection between the user and the topic, in which the connection corresponds to a predicted level of knowledge that the user has about the topic. The online system may later retrieve the user's predicted level of knowledge about the topic (e.g., to select content for presentation to the user).