Abstract: Systems, methods, and non-transitory computer-readable media can generate individual feature data for each user of a plurality of users. A first cohort comprising a first plurality of users is generated, wherein the first plurality of users are selected from the plurality of users based on the individual feature data. A first set of cohort feature data associated with the first cohort is generated based on individual feature data for the first plurality of users. The first set of cohort feature data and a first set of cohort membership information are transmitted to a modeler. The first set of cohort membership information identifies each user of the plurality of users.

Abstract: A cross-measurement system gathers data from a set of publishers to determine cross-publisher statistics about content provided on multiple online platforms associated with different publishers. The cross-measurement system receives a set of publisher-specific Bloom filters associated with content exposure of a content set by each of the multiple publishers. using the received publisher-specific Bloom filters, the cross-measurement system generates a cross-publisher Bloom filter, for example, by combining the publisher-specific Bloom filters. Then, the cross-measurement system analyzes the cross-publisher Bloom filter to determine cross-publisher statistics reflecting the content exposure of the content set across the set of publishers, such as a cross-publisher reach of the content set.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating an identity resolution model from a ground truth data set to accurately match users across one or more digital content providers to perform analyses of user activities across the one or more digital content providers. For example, the systems described herein can generate a ground truth data set of known users and utilize the ground truth data set to generate an identity resolution model for one or more digital content providers based on predicted user identities. Furthermore, in one or more embodiments, the systems utilize the identity resolution model to accurately resolve and match user identities between one or more digital content providers and assign universal identifiers to the user identities. Moreover, the disclosed systems can utilize the universal identifiers to provide analytical insights of user actions between the one or more digital content providers.

Abstract: An online system receives a list of third party user identifiers from a third party attribution system, each associated with one or more user action events by each of a set of users across multiple content providers. The online system determining if it has its own user identifiers that match to at least a subset of the received third party user identifiers. The online system determines relevant impressions corresponding to the matching online system user identifiers, and sends them to the third party attribution system along with an associated subset of the third party user identifiers.

Abstract: An online system receives user-identifying information for an online system user when a request associated with a content item is received from the user at a content publisher. The online system compares this information to user-identifying information maintained in the online system for online system users and adds the received information to user-identifying information maintained in the online system for a user upon finding a match. The online system communicates information describing an association among user-identifying information maintained in the online system and in a partnering third-party system for the user to a trusted third-party system. The trusted third-party system compiles a report based at least in part on this information and information describing requests associated with content items received at content publishers from online system users.