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: One or more embodiments of the present disclosure involve training and utilizing a recall machine learning model to predict recall lift on a per-user basis with respect to digital content items. For example, systems described herein train a recall machine learning model based on poll responses from exposed users and non-exposed users with regard to sample digital content. In particular, the systems described herein train the recall machine learning model to output recall lift scores for a target user based on an assumption that the target user has been exposed to digital content and an assumption that the target user has not been exposed to the digital content. The systems described herein further involve delivering digital content in accordance with the recall lift score.

Abstract: The field of the disclosure relates generally to systems and methods for managing opinion networks with interactive opinion flows and more particularly, but not exclusively, to systems and methods for collecting and analyzing electronic opinion data. A method for analyzing electronic opinion data includes the steps of receiving electronic opinion data, wherein the opinion data includes words of a natural language; mapping the opinion data to unifying opinion objects, the unifying opinion objects provided as a controlled natural language; and providing a presentation having at least one portion corresponding to at least one of said unifying opinions. In an alternative embodiment, the method further includes ranking the unifying opinion objects in an opinion graph to generate per-user relevance.