Abstract: An online system identifies articles containing factual reporting and information associated with the articles (e.g., authors, publishers, distributors, content, etc.). The online system extracts embeddings for the articles based on the information associated with the articles and generates nodes of a graph, in which each node corresponds to an article or information associated with an article. The online system then identifies relationships among the nodes using the embeddings and generates additional nodes of the graph indicating these relationships. Each of the additional nodes may correspond to any type of information that may be associated with an article. The online system may query the graph for information identifying publishers that published articles alleging a fact, information identifying articles containing editorialized content or clickbait, etc. or to identify and remove similar articles from a feed to be presented to an online system user, to highlight contradicting articles in the feed, etc.

Abstract: Systems, methods, and non-transitory computer-readable media can train a machine learning model to output respective scores for content items based at least in part on information describing a user, wherein a score for a content item measures a likelihood that the user will select the content item to be included in a social profile of the user. A determination is made that a first user of the social networking system is eligible for a content item suggestion. A first content item to be provided as a suggestion to the first user is determined based at least in part on the model. The first content item is provided as a suggestion to the first user for use in a social profile of the first user.

Abstract: Systems, methods, and non-transitory computer-readable media can train a machine learning model to output respective scores for content items based at least in part on information describing a user, wherein a score for a content item measures a likelihood that the user will select the content item to be included in a social profile of the user. A determination is made that a first user of the social networking system is eligible for a content item suggestion. A first content item to be provided as a suggestion to the first user is determined based at least in part on the model. The first content item is provided as a suggestion to the first user for use in a social profile of the first user.

Abstract: An audio system, such as an audio system included in a virtual reality system, includes multiple bone transducers contacting portions of a user's head. Each bone transducer is a device, such as a piezoelectric device, that vibrates to induce vibration of bones in the user's head contacting a bone transducer. Vibration of the bone transducers mimic vibration of the bones in the user's head caused by acoustic waves contacting the user's head. During an initial calibration process, one or more models for generating control signals to vibrate the bone transducers are determined by capturing vibrations of bones in the user's head caused by audio data having different frequencies, amplitudes, phase variations, and originating from different positions relative to the user's head.

Abstract: An audio system, such as an audio system included in a virtual reality system, includes multiple bone transducers contacting portions of a user's head. Each bone transducer is a device, such as a piezoelectric device, that vibrates to induce vibration of bones in the user's head contacting a bone transducer. Vibration of the bone transducers mimic vibration of the bones in the user's head caused by acoustic waves contacting the user's head. During an initial calibration process, one or more models for generating control signals to vibrate the bone transducers are determined by capturing vibrations of bones in the user's head caused by audio data having different frequencies, amplitudes, phase variations, and originating from different positions relative to the user's head.