Abstract: An online system receives posts that include links to various external pages and presents those posts to users of the online system. When the online system determines an opportunity to present a post to a particular viewing user of the online system, the online system determines a quality metric and an associated value score for the post. The quality metric is determined as a likelihood that the viewing user will view the external page for less than a threshold time period, and is used to adjust the associated value score. The online system compares the value score of the post to the value scores of other posts and selects one or more of the compared posts for presentation to the viewing user of the online system.

Abstract: The disclosed computer-implemented method may include (1) sampling links from an online system, (2) receiving, from a human labeler for each of the links, a label indicating whether the human labeler considers a landing page of the link to be a low-quality webpage, (3) deriving features from a landing page of each of the links, (4) using the label and the features of each of the links to train a model configured to predict a likelihood that a link is to a low-quality webpage, (5) identifying content items that are candidates for a content feed of a user of the online system, (6) applying the model to a link of each of the content items to determine a ranking of the content items, and (7) displaying the content items in the content feed of the user based on the ranking. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: An online system receives posts that include links to various external pages and presents those posts to users of the online system. When the online system determines an opportunity to present a post to a particular viewing user of the online system, the online system determines a quality metric and an associated value score for the post. The quality metric is determined as a likelihood that the viewing user will view the external page for less than a threshold time period, and is used to adjust the associated value score. The online system compares the value score of the post to the value scores of other posts and selects one or more of the compared posts for presentation to the viewing user of the online system.

Abstract: Systems, methods, and non-transitory computer-readable media can select one or more content items that are associated with one or more interactions that each at least meet a specified interaction metric threshold. Data associated with the one or more content items can be acquired. A classifier can be developed based on the data associated with the one or more content items. At least some of the one or more content items can be identified, based on the classifier, as having at least a threshold confidence score of being interaction-bait content.