SYSTEMS AND METHODS FOR EVENT BROADCASTS

1 . A computer-implemented method comprising:

determining, by a computing system, a broadcaster request to determine information for conducting a content broadcast through the computing system;

determining, by the computing system, one or more parameters for the broadcast using a machine learning model that has been trained to predict the one or more parameters based at least in part on data describing previously conducted broadcasts; and

providing, by the computing system, information to the broadcaster that describes at least the one or more parameters.

2 . The computer-implemented method of claim 1 , wherein determining the one or more parameters for the broadcast using the machine learning model further comprises:

determining, by the computing system, at least one time period for conducting the broadcast based at least in part on the model, wherein at least a threshold number of users are expected to access the broadcast for at least a portion of the time period.

3 . The computer-implemented method of claim 1 , wherein determining the one or more parameters for the broadcast using the machine learning model further comprises:

determining, by the computing system, at least one topic for the broadcast based at least in part on the model, wherein at least a threshold number of users are expected to access the broadcast when conducted on the at least one topic.

4 . The computer-implemented method of claim 3 , wherein the at least one topic for the broadcast is automatically generated based on at least one of: information specified in a social profile of the broadcaster, topics corresponding to posts that were published by the broadcaster through the computing system, or a geographic location corresponding to the broadcaster.

5 . The computer-implemented method of claim 1 , wherein determining the one or more parameters for the broadcast using the machine learning model further comprises:

determining, by the computing system, at least one geographic location from which to conduct the broadcast based at least in part on the model, wherein at least a threshold number of users are expected to access the broadcast when conducted from the at least one geographic location.

6 . The computer-implemented method of claim 1 , wherein determining the one or more parameters for the broadcast using the machine learning model further comprises:

determining, by the computing system, information describing users that are expected to access the broadcast based at least in part on the model.

7 . The computer-implemented method of claim 6 , wherein the information includes at least one of: the number of users or demographic information describing the users.

8 . The computer-implemented method of claim 1 , the method further comprising:

causing, by the computing system, an audience for the broadcast to be built, wherein the audience comprises a set of users that are interested in the broadcast;

determining, by the computing system, that a size of the audience satisfies a threshold; and

providing, by the computing system, at least one notification to the broadcaster, the notification describing the set of users that are interested in the broadcast.

9 . The computer-implemented method of claim 8 , wherein causing the audience for the broadcast to be built further comprises:

providing, by the computing system, one or more notifications to the set of users to inform the users about the broadcast.

10 . The computer-implemented method of claim 8 , wherein causing the audience for the broadcast to be built further comprises:

providing, by the computing system, one or more polling questionnaires to the set of users to inform the users about the broadcast and determine a number of the users that are interested in the broadcast.

11 . A system comprising:

at least one processor; and

a memory storing instructions that, when executed by the at least one processor, cause the system to perform:

determining a broadcaster request to determine information for conducting a content broadcast through the computing system;

determining one or more parameters for the broadcast using a machine learning model that has been trained to predict the one or more parameters based at least in part on data describing previously conducted broadcasts; and

providing information to the broadcaster that describes at least the one or more parameters.

12 . The system of claim 11 , wherein determining the one or more parameters for the broadcast using the machine learning model further causes the system to perform:

determining at least one time period for conducting the broadcast based at least in part on the model, wherein at least a threshold number of users are expected to access the broadcast for at least a portion of the time period.

13 . The system of claim 11 , wherein determining the one or more parameters for the broadcast using the machine learning model further causes the system to perform:

determining at least one topic for the broadcast based at least in part on the model, wherein at least a threshold number of users are expected to access the broadcast when conducted on the at least one topic.

14 . The system of claim 13 , wherein the at least one topic for the broadcast is automatically generated based on at least one of: information specified in a social profile of the broadcaster, topics corresponding to posts that were published by the broadcaster through the computing system, or a geographic location corresponding to the broadcaster.

15 . The system of claim 11 , wherein determining the one or more parameters for the broadcast using the machine learning model further causes the system to perform:

determining at least one geographic location from which to conduct the broadcast based at least in part on the model, wherein at least a threshold number of users are expected to access the broadcast when conducted from the at least one geographic location.

16 . A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising:

determining a broadcaster request to determine information for conducting a content broadcast through the computing system;

determining one or more parameters for the broadcast using a machine learning model that has been trained to predict the one or more parameters based at least in part on data describing previously conducted broadcasts; and

providing information to the broadcaster that describes at least the one or more parameters.

17 . The non-transitory computer-readable storage medium of claim 16 , wherein determining the one or more parameters for the broadcast using the machine learning model further causes the computing system perform:

determining at least one time period for conducting the broadcast based at least in part on the model, wherein at least a threshold number of users are expected to access the broadcast for at least a portion of the time period.

18 . The non-transitory computer-readable storage medium of claim 16 , wherein determining the one or more parameters for the broadcast using the machine learning model further causes the computing system perform:

determining at least one topic for the broadcast based at least in part on the model, wherein at least a threshold number of users are expected to access the broadcast when conducted on the at least one topic.

19 . The non-transitory computer-readable storage medium of claim 18 , wherein the at least one topic for the broadcast is automatically generated based on at least one of: information specified in a social profile of the broadcaster, topics corresponding to posts that were published by the broadcaster through a social networking system, or a geographic location corresponding to the broadcaster.

20 . The non-transitory computer-readable storage medium of claim 16 , wherein determining the one or more parameters for the broadcast using the machine learning model further causes the computing system perform:

determining at least one geographic location from which to conduct the broadcast based at least in part on the model, wherein at least a threshold number of users are expected to access the broadcast when conducted from the at least one geographic location.