Abstract: A social networking system receives messages from users that include hashtags. The social networking system may use a natural language model to identify terms in the hashtag corresponding to words or phrases of the hashtag. The words or phrases may be used to modify a string of the hashtag. The social networking system may also generate computer models to determine likely membership of a message with various hashtags. Prior to generating the computer models, the social networking system may filter certain hashtags from eligibility for computer modeling, particularly hashtags that are not frequently used or that more typically appear as normal text in a message instead of as a hashtag. The social networking system may also calibrate the computer model outputs by comparing a test message output with outputs of a calibration group that includes positive and negative examples with respect to the computer model output.

Abstract: A social networking system receives messages from users that include hashtags. The social networking system may use a natural language model to identify terms in the hashtag corresponding to words or phrases of the hashtag. The words or phrases may be used to modify a string of the hashtag. The social networking system may also generate computer models to determine likely membership of a message with various hashtags. Prior to generating the computer models, the social networking system may filter certain hashtags from eligibility for computer modeling, particularly hashtags that are not frequently used or that more typically appear as normal text in a message instead of as a hashtag. The social networking system may also calibrate the computer model outputs by comparing a test message output with outputs of a calibration group that includes positive and negative examples with respect to the computer model output.

Abstract: A social networking system receives messages from users that include hashtags. The social networking system may use a natural language model to identify terms in the hashtag corresponding to words or phrases of the hashtag. The words or phrases may be used to modify a string of the hashtag. The social networking system may also generate computer models to determine likely membership of a message with various hashtags. Prior to generating the computer models, the social networking system may filter certain hashtags from eligibility for computer modeling, particularly hashtags that are not frequently used or that more typically appear as normal text in a message instead of as a hashtag. The social networking system may also calibrate the computer model outputs by comparing a test message output with outputs of a calibration group that includes positive and negative examples with respect to the computer model output.

Abstract: A social networking system generates a newsfeed for a user to view when accessing the social networking system. Candidate stories associated with users of the social networking system are selected and an expected value score for each candidate story is determined. An expected value score is based on the probability of a user performing various types of interactions with a candidate story and a numerical value for each type of interaction. The numerical value for a type of interaction represents a value to the social networking system of the type of interaction. Based on the expected value scores, the candidate stories are ranked and the ranking used to select candidate stories for the newsfeed.

Abstract: Systems, methods, and non-transitory computer readable media configured to determine a value of a utility factor associated with a content item corresponding to a link. An optimized utility value relating to an interaction type of an outbound click is determined based on the value of the utility factor. An expected utility score associated with the content item is generated based on the optimized utility value to determine potential presentation of the content item to a user.

Abstract: A social networking system classifies content items according to their qualities for ranking and selection of content items to present to users within, for example, a newsfeed. Low-quality content items that are unlikely to be interesting or relevant to a user may be distinguished though they may appear to be popular among users in the social networking system. The social networking system identifies within the content items one or more features that are indicators of the quality of the content items. The social networking system can use one or more classifiers to evaluate the content items based on the features, and it can compute a quality metric indicating the quality of a content item based on the result obtained from the classifiers. The quality metric can be used in the ranking and selection of a set of content items to provide to the user.

Abstract: A social networking system generates a newsfeed for a user to view when accessing the social networking system. Candidate stories associated with users of the social networking system are selected and an expected value score for each candidate story is determined. An expected value score is based on the probability of a user performing various types of interactions with a candidate story and a numerical value for each type of interaction. The numerical value for a type of interaction represents a value to the social networking system of the type of interaction. Based on the expected value scores, the candidate stories are ranked and the ranking used to select candidate stories for the newsfeed.

Abstract: A social networking system receives messages from users that include links to webpages that designate keywords of the webpage. The social networking system identifies webpages linked by users to generate computer models that predict whether a webpage or message should be associated with particular keywords. The social networking system generates computer models that are trained on example webpages and related keywords linked by users in messages. Prior to generating computer models, the social networking system applies one or more filters to exclude webpages and keywords from consideration. The filters may exclude webpages that have low-reliability, are associated with an excessive number of keywords, or keywords that appear on an insufficient number of domains. After training the computer models, messages composed by users may be analyzed and a keyword predicted for the message, which may be suggested to the user to categorize the message.

Abstract: Systems, methods, and non-transitory computer readable media configured to detect access by a user to an original content item relating to a story. At least one of a comments based technique, a token based technique, and a tag based technique is performed on content items. Constraints are applied to identify at least one follow up content item from the content items relating to a development of the story.

Abstract: Systems, methods, and non-transitory computer-readable media can identify a source content item for which related content is to be provided. A set of candidate content items associated with the source content item can be selected. The set of candidate content items can be ranked based, at least in part, on a set of engagement signals associated with the set of candidate content items. A subset of highest ranked candidate content items out of the set of candidate content items can be provided as the related content for the source content item.

Abstract: A social networking system generates stories based on actions of users in the system and provides a newsfeed to users that contain stories that related to one or more of their friends in the system. Although the story ranking algorithm includes a time decay to penalize older stories, stories may actually become stale at different rates. To measure the staleness of a story, the system computes a ratio of a current engagement rate for the story to an average engagement rate for the story. Based on this ratio, the system may filter out stale stories, includes the ratio as a feature in the scoring model, and/or adjust the decay rate.

Abstract: A social networking system generates a newsfeed for a user to view when accessing the social networking system. Candidate stories associated with users of the social networking system are selected and attributes of each story are determined. The candidate stories are ranked so that the ranking of a candidate story having one or more common attributes with another candidate story is modified. This reduces the likelihood of the newsfeed presenting candidate stories with common attributes proximate to each other.

Abstract: Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story.

Abstract: Exemplary methods, apparatuses, and systems determine first and second entities within a social networking system are each associated with a topic. A relationship between the first entity and the second entity is detected. The first entity is determined to be an authority on the topic based upon the detected relationship between the first entity and the second entity. In response to detecting an indication that a user of the social networking system may be interested in the topic, the user is presented with content posted to the social networking system by the first entity based upon determining the first authority is an authority on the topic.

Abstract: Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story.

Abstract: Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story.

Abstract: A social networking system generates a newsfeed for a user to view when accessing the social networking system. Candidate stories associated with users of the social networking system are selected and attributes of each story are determined. The candidate stories are ranked so that the ranking of a candidate story having one or more common attributes with another candidate story is modified. This reduces the likelihood of the newsfeed presenting candidate stories with common attributes proximate to each other.

Abstract: A social networking system generates a newsfeed for a user to view when accessing the social networking system. Candidate stories associated with users of the social networking system are selected and an expected value score for each candidate story is determined. An expected value score is based on the probability of a user performing various types of interactions with a candidate story and a numerical value for each type of interaction. The numerical value for a type of interaction represents a value to the social networking system of the type of interaction. Based on the expected value scores, the candidate stories are ranked and the ranking used to select candidate stories for the newsfeed.

Abstract: A social networking system generates a newsfeed for a user to view when accessing the social networking system. Candidate stories associated with users of the social networking selection are selected for inclusion in the newsfeed. Based on data associated with users associated with candidate stories, the social networking system determines a neediness value for the users associated with the candidate stories. The neediness value of a user indicates a degree of assistance by the social networking system to distribute content associated with the user. For users indicated as “needy users” based on their neediness values, the social networking system modifies the location of candidate stories associated with needy users in a ranking of the candidate stories.

Abstract: Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story.