Systems, methods, and non-transitory computer-readable media train a machine learning model to forecast growth of a content item, the growth being measured based at least in part on a count of user interactions with the content item, wherein the model is trained to adjust growth forecasts for the content item in response to one or more users interacting with the content item. A first growth forecast for the content item can be determined for a unit of time using the machine learning model. A determination is made that a first user has interacted with the content item. A second growth forecast for the content item can be determined for the unit of time using the machine learning model and based at least in part on the first user interacting with the content item.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: training, by a computing system, a machine learning model to forecast growth of a content item, the growth being measured based at least in part on a count of user interactions with the content item, wherein the model is trained to adjust growth forecasts for the content item in response to one or more users interacting with the content item; determining, by the computing system, a first growth forecast for the content item for a unit of time using the machine learning model; determining, by the computing system, that a first user has interacted with the content item; and determining, by the computing system, a second growth forecast for the content item for the unit of time using the machine learning model and based at least in part on the first user interacting with the content item. 2. The computer-implemented method of claim 1, wherein the content item corresponds to at least one of a page, post, media item, or link that is published through the computing system. 3. The computer-implemented method of claim 1, wherein the user interaction corresponds to at least one of a selection of a like option, a selection of a check-in option, posting content, or sharing content. 4. The computer-implemented method of claim 1, wherein the machine learning model is trained using user interactions that are measured for a group of users of a particular demographic, and wherein the machine learning model predicts growth of the content item based on interactions from users in the particular demographic. 5. The computer-implemented method of claim 1, wherein training the machine learning model further comprises: generating, by the computing system, a set of training examples that each correspond to a unit of time and include an outcome, a position value determined for the unit of time, a velocity value determined for the unit of time, an acceleration value determined for the unit of time, and respective values indicating which users interacted with the content item during the unit of time. 6. The computer-implemented method of claim 5, wherein generating the set of training examples further comprises: generating, by the computing system, a growth curve for the content item, the growth curve plotting a growth of the content item for each unit of time over a period of time; determining, by the computing system, the position value for the unit of time based at least in part on the growth curve; determining, by the computing system, the velocity value for the unit of time based at least in part on the growth curve; and determining, by the computing system, the acceleration value for the unit of time based at least in part on the growth curve. 7. The computer-implemented method of claim 1, wherein determining the first growth forecast for the content item for the unit of time further comprises: providing, by the computing system, a set of input values that include values describing a shape of a growth curve for the content item at a preceding unit of time and respective values indicating which users have interacted with the content item by the preceding the unit of time, wherein the respective value for the first user indicates that the first user has not interacted with the content item. 8. The computer-implemented method of claim 1, wherein determining the second growth forecast for the content item for the unit of time further comprises: providing, by the computing system, a set of input values that include values describing a shape of a growth curve for the content item at a preceding unit of time and respective values indicating which users have interacted with the content item at the preceding the unit of time, wherein the respective value for the first user indicates that the first user has interacted with the content item. 9. The computer-implemented method of claim 1, wherein the second growth forecast is adjusted in response to the first user having interacted with the content item. 10. The computer-implemented method of claim 10, wherein the machine learning model is trained to adjust the second growth forecast based at least in part on a step function, linear growth function, or a growth curve function. 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: training a machine learning model to forecast growth of a content item, the growth being measured based at least in part on a count of user interactions with the content item, wherein the model is trained to adjust growth forecasts for the content item in response to one or more users interacting with the content item; determining a first growth forecast for the content item for a unit of time using the machine learning model; determining that a first user has interacted with the content item; and determining a second growth forecast for the content item for the unit of time using the machine learning model and based at least in part on the first user interacting with the content item. 12. The system of claim 11, wherein the content item corresponds to at least one of a page, post, media item, or link that is published through the computing system. 13. The system of claim 11, wherein the user interaction corresponds to at least one of a selection of a like option, a selection of a check-in option, posting content, or sharing content. 14. The system of claim 11, wherein the machine learning model is trained using user interactions that are measured for a group of users of a particular demographic, and wherein the machine learning model predicts growth of the content item based on interactions from users in the particular demographic. 15. The system of claim 11, wherein training the machine learning model further causes the system to perform: generating a set of training examples that each correspond to a unit of time and include an outcome, a position value determined for the unit of time, a velocity value determined for the unit of time, an acceleration value determined for the unit of time, and respective values indicating which users interacted with the content item during the unit of time. 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: training a machine learning model to forecast growth of a content item, the growth being measured based at least in part on a count of user interactions with the content item, wherein the model is trained to adjust growth forecasts for the content item in response to one or more users interacting with the content item; determining a first growth forecast for the content item for a unit of time using the machine learning model; determining that a first user has interacted with the content item; and determining a second growth forecast for the content item for the unit of time using the machine learning model and based at least in part on the first user interacting with the content item. 17. The non-transitory computer-readable storage medium of claim 16, wherein the content item corresponds to at least one of a page, post, media item, or link that is published through the computing system. 18. The non-transitory computer-readable storage medium of claim 16, wherein the user interaction corresponds to at least one of a selection of a like option, a selection of a check-in option, posting content, or sharing content. 19. The non-transitory computer-readable storage medium of claim 16, wherein the machine learning model is trained using user interactions that are measured for a group of users of a particular demographic, and wherein the machine learning model predicts growth of the content item based on interactions from users in the particular demographic. 20. The non-transitory computer-readable storage medium of claim 16, wherein training the machine learning model further causes the computing system to perform: generating a set of training examples that each correspond to a unit of time and include an outcome, a position value determined for the unit of time, a velocity value determined for the unit of time, an acceleration value determined for the unit of time, and respective values indicating which users interacted with the content item during the unit of time.