Abstract: The present disclosure is directed towards systems and methods for efficiently identifying periodic trends of user-defined annotations among users of a social networking system. For example, systems and methods described herein involve grouping a set of digital content items into subsets of digital content items and performing one or more iterations of a clustering algorithm on the grouped set of digital content items to identify one or more temporal clusters of the user-defined annotation. Additionally, the systems and methods described herein involve performing one or more additional iterations of the clustering algorithm on one or more reduced sets of digital content items to identify one or more additional temporal clusters of the user-defined annotation. Further, the systems and methods involve determining that the user-defined annotation is periodic based on a time period between identified temporal clusters.

Abstract: One or more embodiments of the disclosure include a character management system that determines and utilizes contextual meaning with regard to standardized image characters. In particular, the character management system determines contextual meaning with regard to standardized characters based on the manner in which the standardized characters are utilized in a repository of electronic documents. Moreover, the character management system can utilize the contextual meaning to search, recommend, analyze, and translate with regard to the use of standardized image characters.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire a set of labels associated with a set of content items. Each label in the set of labels can be associated with at least one content item in the set of content items. It can be determined that at least two labels, out of the set of labels, are related. The at least two labels can be determined to be related based on at least one of a co-occurrence metric associated with the at least two labels or a topic similarity metric associated with the at least two labels. One label can be selected, out of the at least two labels, as being representative of the at least two labels.

Abstract: One or more embodiments of the disclosure include a character management system that determines and utilizes contextual meaning with regard to standardized image characters. In particular, the character management system determines contextual meaning with regard to standardized characters based on the manner in which the standardized characters are utilized in a repository of electronic documents. Moreover, the character management system can utilize the contextual meaning to search, recommend, analyze, and translate with regard to the use of standardized image characters.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire a set of labels associated with a set of content items. Each label in the set of labels can be associated with at least one content item in the set of content items. It can be determined that at least two labels, out of the set of labels, are related. The at least two labels can be determined to be related based on at least one of a co-occurrence metric associated with the at least two labels or a topic similarity metric associated with the at least two labels. One label can be selected, out of the at least two labels, as being representative of the at least two labels.

Abstract: One or more embodiments of the disclosure include a character management system that determines and utilizes contextual meaning with regard to standardized image characters. In particular, the character management system determines contextual meaning with regard to standardized characters based on the manner in which the standardized characters are utilized in a repository of electronic documents. Moreover, the character management system can utilize the contextual meaning to search, recommend, analyze, and translate with regard to the use of standardized image characters.

Abstract: Systems, methods, and non-transitory computer-readable media can determine training data describing respective relationships between a set of map tiles, the map tiles collectively representing a given geographic region. A model can be trained to predict a likelihood of a pair of map tiles corresponding to one or more geographic classifications based at least in part on the training data. Polygons that correspond to respective sub-regions within the geographic region can be determined based at least in part on the model.

Abstract: The present disclosure is directed towards systems and methods for efficiently identifying periodic trends of user-defined annotations among users of a social networking system. For example, systems and methods described herein involve grouping a set of digital content items into subsets of digital content items and performing one or more iterations of a clustering algorithm on the grouped set of digital content items to identify one or more temporal clusters of the user-defined annotation. Additionally, the systems and methods described herein involve performing one or more additional iterations of the clustering algorithm on one or more reduced sets of digital content items to identify one or more additional temporal clusters of the user-defined annotation. Further, the systems and methods involve determining that the user-defined annotation is periodic based on a time period between identified temporal clusters.

Abstract: Systems, methods, and non-transitory computer readable media configured to acquire data associated with a content item, the data associated with the content item including contextual information. The data associated with the content item can be provided to a model trained by machine learning. A set of hashtags associated with the content item can be determined based on the model.

Abstract: One or more embodiments of the disclosure include a character management system that determines and utilizes contextual meaning with regard to standardized image characters. In particular, the character management system determines contextual meaning with regard to standardized characters based on the manner in which the standardized characters are utilized in a repository of electronic documents. Moreover, the character management system can utilize the contextual meaning to search, recommend, analyze, and translate with regard to the use of standardized image characters.

Abstract: One or more embodiments of the disclosure include a character management system that determines and utilizes contextual meaning with regard to standardized image characters. In particular, the character management system determines contextual meaning with regard to standardized characters based on the manner in which the standardized characters are utilized in a repository of electronic documents. Moreover, the character management system can utilize the contextual meaning to search, recommend, analyze, and translate with regard to the use of standardized image characters.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire shape data representative of one or more polygons. At least one serialization format can be produced based on the shape data. The at least one serialization format can be packaged into a proprietary package that is representative of the one or more polygons. At least a portion of the proprietary package can be utilized for one or more applications.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire a set of labels associated with a set of content items. Each label in the set of labels can be associated with at least one content item in the set of content items. It can be determined that at least two labels, out of the set of labels, are related. The at least two labels can be determined to be related based on at least one of a co-occurrence metric associated with the at least two labels or a topic similarity metric associated with the at least two labels. One label can be selected, out of the at least two labels, as being representative of the at least two labels.

Abstract: In one embodiment, a method includes a computing device receiving postings from users of an online social networking system. A postings may include location data along with one or more tags that may describe the content of the posting. The computing device may identify regions and subregions from which the postings originated, and may determine a distribution of the tags according to two data dimensions: the ubiquity of the tags across the regions, and the ubiquity of the tags across the subregions. Based on the distribution, the computing device may create a neighborhood characterization to accurately describe one or more subregions. The computing device may also determine applications for the neighborhood characterization.

Abstract: Systems, methods, and non-transitory computer-readable media can acquire a set of labels associated with a set of content items. Each label in the set of labels can be associated with at least one content item in the set of content items. It can be determined that at least two labels, out of the set of labels, are related. The at least two labels can be determined to be related based on at least one of a co-occurrence metric associated with the at least two labels or a topic similarity metric associated with the at least two labels. One label can be selected, out of the at least two labels, as being representative of the at least two labels.

Abstract: Methods, systems, computer-readable media, and apparatuses for using, by one or more computer devices, engagement actions by users of a social network as behavioral signals for evaluating the quality of a content item shared by those users. Once the quality of a content item has been evaluated in this manner, an action can be performed on that content item (e.g., changing the visibility of that content item within the social network).

Abstract: Some embodiments include a method of detecting and analyzing virally propagating subject matter in a social networking system. The method includes processing user activities in the social networking system through a relevancy filter to identify a subset of user activities that are relevant to a viral propagation study. The social networking system can construct, in response to selecting a user activity as a graph exploration seed, a user activity cascade by exploring the social graph in the social networking system, starting from a social network node corresponding to the user activity. The user activity cascade can comprise social network nodes found during the graph exploration. The social networking system can determine that the user activity cascade is virally propagating based at least upon a total size of the user activity cascade.

Abstract: Systems, methods, and non-transitory computer-readable media can generate a set of clusters using sample content items in which a set of user features are represented, the sample content items being clustered based at least in part on their similarity to one another; obtain one or more content items that capture a set of user features corresponding to a given user; determine that the user corresponds to a given cluster in the set of clusters based at least in part on the features of the user; and assign an avatar associated with the cluster to the user.

Abstract: Systems, methods, and non-transitory computer-readable media can generate a set of clusters using sample content items in which a set of user features are represented, the sample content items being clustered based at least in part on their similarity to one another; obtain one or more content items that capture a set of user features corresponding to a given user; determine that the user corresponds to a given cluster in the set of clusters based at least in part on the features of the user; and assign an avatar associated with the cluster to the user.

Abstract: Systems, methods, and non-transitory computer-readable media can determine training data describing respective relationships between a set of map tiles, the map tiles collectively representing a given geographic region. A model can be trained to predict a likelihood of a pair of map tiles corresponding to one or more geographic classifications based at least in part on the training data. Polygons that correspond to respective sub-regions within the geographic region can be determined based at least in part on the model.