Abstract: Systems and methods are described herein for determining identifying features across messages communicated in a network. In examples, a system can be configured to receive first activity data associated with a first set of messages involving a first channel and extract feature vectors for each message. The system can receive second activity data associated with a second set of messages associated with a second channel, extract feature vectors for each message of the second set of messages, and compare a first feature vector from the first set of feature vectors with a second feature vector from the second set of feature vectors. A correlation value can be determined based on the comparison of the first feature vector with the second feature vector and the system can provide display data to the first device or the second device based on the correlation value satisfying a device or user correlation threshold.

Abstract: Systems and methods described herein may determine audiences for content delivery. A server receives configuration inputs for identifying a target audience from a content-generating user. The configuration inputs indicate the target audience, baseline audience, and special audience. The target audience includes targeted end-users for the content. The baseline audience includes a broad population of end-users having a population characteristic (e.g., geography). The special audience includes a selected population of end-users having a special characteristic in the topics previously accessed by the end-users, such that the server correlates the baseline audience with the special audience to identify the target audience of target users who accessed a webpage having the topic and who are located in, e.g., a geography. The server uses topics terms from all target users to generate a ranked-list of context terms the content-user applies to predict whether webpages have the target audience.

Abstract: Systems and methods described herein may determine audiences for content delivery. A server receives configuration inputs for identifying a target audience from a content-generating user. The configuration inputs indicate the target audience, baseline audience, and special audience. The target audience includes targeted end-users for the content. The baseline audience includes a broad population of end-users having a population characteristic (e.g., geography). The special audience includes a selected population of end-users having a special characteristic in the topics previously accessed by the end-users, such that the server correlates the baseline audience with the special audience to identify the target audience of target users who accessed a webpage having the topic and who are located in, e.g., a geography. The server uses topics terms from all target users to generate a ranked-list of context terms the content-user applies to predict whether webpages have the target audience.

Abstract: Systems and methods described herein may generate campaigns and efficiently calculate bids for placement of campaign data into Internet data. Embodiments may calculate context scores for campaign data based on campaign terms and beacon terms. The context scores may be used to identify Internet content that has a high page score. If the page score of particular Internet content exceeds a predetermined threshold, the system may place a bid for a campaign based on disclosed algorithms taking as inputs performance scores, context scores, page scores, campaign budgets, and other parameters. The systems therefore are capable of quickly and effectively calculating optimal bids to place for a particular campaign given parameters disclosed herein.