Abstract: Techniques are described for granting access to a restricted graphic design icon to use for generating modified content to be shared between users. For example, user interfaces are described that include features and/or mode controls for generating content associated with a restricted graphic design icon limited to accounts that meet a set of characteristics that can then be shared between users.

Abstract: Techniques for generating ephemeral collections of content items are described herein. For example, a social networking system may receive, from a first account, a request to generate a collection of content items, or a “collaborative story.” The request may include an identifier associated with a second account that is to be associated with the collaborative story. Based on the request, the social networking system may generate the collaborative story and associate the second account therewith, designating the first account and the second account are as collaborators. In some examples, the social networking system may receive content from the first account and/or the second account and may publish the content in association with the collaborative story via the social networking system.

Abstract: Systems, methods, and non-transitory computer-readable media can determine that a broadcaster of a first live content stream has identified at least one user of a social networking system to join as co-broadcaster. A merged live content stream is published through the social networking system, the merged live content stream including the first live content stream of the broadcaster and a second live content stream associated with the at least one identified user. At least one notification is sent to one or more other users of the social networking system. The notification informs the one or more other users about the merged live content stream.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating accurate digital media clusters corresponding to predicted distribution classes from a repository of digital media based on network distribution history. For example, a digital media clustering system can apply machine learning models at a remote server (based on network distribution history of a network account of a user) to generate predicted distribution classes for future electronic communications. The remote server can provide the predicted distribution classes to a user client device for secure local analysis of digital media stored at the client device. Based on the predicted distribution classes and the stored digital media, the client device can suggest digital media items to distribute via a networking system. Thus, the disclosed system can surface digital media content without providing any information regarding the digital media items stored at the client device to a remote server.

Abstract: Systems, methods, and non-transitory computer-readable media can determine that broadcasting of a live content stream has concluded. The concluded live content stream is converted to a post live media content item. The post live media content item corresponding to the concluded live content stream is published through the social networking system. The post live media content item remains accessible through the social networking system for a pre-determined period of time.

Abstract: Systems, methods, and non-transitory computer-readable media can determine that a broadcaster of a first live content stream has identified at least one user of a social networking system to join as co-broadcaster. A merged live content stream is published through the social networking system, the merged live content stream including the first live content stream of the broadcaster and a second live content stream associated with the at least one identified user. At least one notification is sent to one or more other users of the social networking system. The notification informs the one or more other users about the merged live content stream.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating accurate digital media clusters corresponding to predicted distribution classes from a repository of digital media based on network distribution history. For example, a digital media clustering system can apply machine learning models at a remote server (based on network distribution history of a network account of a user) to generate predicted distribution classes for future electronic communications. The remote server can provide the predicted distribution classes to a user client device for secure local analysis of digital media stored at the client device. Based on the predicted distribution classes and the stored digital media, the client device can suggest digital media items to distribute via a networking system. Thus, the disclosed system can surface digital media content without providing any information regarding the digital media items stored at the client device to a remote server.