INCREASING AUDIENCE EXPOSURE FOR BEGINNING CREATORS

1 . A computer-implemented method for promoting social media content, comprising:

receiving user-created content for a social media platform;

tracking a performance of the user-created content;

in response to the performance breaching a predefined threshold, promoting the user-created content to a higher tier level, the higher tier level associated with a higher performance threshold than the predefined threshold;

tracking the performance of the user-created content at the higher tier level;

in response to the performance breaching the higher performance threshold of the higher tier level, promoting the user-created content to an even higher tier level, the even higher tier level associated with an even higher performance threshold than the higher performance threshold;

training a machine learning model on example input-output pairs, each example input-output pair comprising a representation of the user-created content and the performance that breaches at least the predefined threshold; and

determining, through the machine learning model, whether a popularity of the user-created content will grow exponentially.

2 . The computer-implemented method of claim 1 , wherein the user-created content comprises at least one of a video, photo, image, multimedia, linked content, text, or post.

3 . The computer-implemented method of claim 1 , wherein the performance is determined based on at least one of clicks, likes, comments, shares, views, or reads.

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

performing an eligibility check when the user-created content is posted.

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

removing the user-created content from a curated group when the performance fails to breach the predefined threshold.

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

adding the user-created content to a curated group where the user-created content has a high probability of performing above the predefined threshold.

7 . The computer-implemented method of claim 6 , wherein the curated group comprises users with similar personal or career interests, activities, backgrounds, social media connections, or real-life connections to each other.

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

causing display of the performance through a user interface.

9 . The computer-implemented method of claim 1 , wherein a curated list comprises at least the user-created content.

10 . The computer-implemented method of claim 1 , wherein the machine learning model is utilized to predict performance statistics of the user-created content.

11 . A system configured for promoting social media content, the system comprising:

one or more hardware processors configured by machine-readable instructions to:

receive user-created content for a social media platform, the user-created content comprising at least one of a video, photo, image, multimedia, linked content, text, or post;

track a performance of the user-created content;

in response to the performance breaching a predefined threshold, promote the user-created content to a higher tier level, the higher tier level associated with a higher performance threshold than the predefined threshold;

track the performance of the user-created content at the higher tier level;

in response to the performance breaching the higher performance threshold of the higher tier level, promote the user-created content to an even higher tier level, the even higher tier level associated with an even higher performance threshold than the higher performance threshold;

training a machine learning model on example input-output pairs, each example input-output pair comprising a representation of the user-created content and the performance that breaches at least the predefined threshold;

determining, through the machine learning model, whether a popularity of the user-created content will grow exponentially; and

predicting, through the machine learning model, performance statistics of the user-created content.

12 . The system of claim 11 , wherein the performance is determined based on at least one of clicks, likes, comments, shares, views, or reads.

13 . The system of claim 11 , wherein the one or more hardware processors are further configured by machine-readable instructions to:

perform an eligibility check when the user-created content is posted.

14 . The system of claim 11 , wherein the one or more hardware processors are further configured by machine-readable instructions to:

remove the user-created content from a curated group when the performance fails to breach the predefined threshold.

15 . The system of claim 11 , wherein the one or more hardware processors are further configured by machine-readable instructions to:

add the user-created content to a curated group where the user-created content has a high probability of performing above the predefined threshold.

16 . The system of claim 15 , wherein the curated group comprises users with similar personal or career interests, activities, backgrounds, social media connections, or real-life connections to each other.

17 . The system of claim 11 , wherein the one or more hardware processors are further configured by machine-readable instructions to:

cause display of the performance through a user interface.

18 . The system of claim 11 , wherein a curated list comprises at least the user-created content.

19 . A non-transient computer-readable storage medium having instructions embodied thereon, the instructions being executable by one or more processors to perform a method for promoting social media content, the method comprising:

receiving user-created content for a social media platform;

performing an eligibility check when the user-created content is received;

tracking a performance of the user-created content;

in response to the performance breaching a predefined threshold, promoting the user-created content to a higher tier level, the higher tier level associated with a higher performance threshold than the predefined threshold;

tracking the performance of the user-created content at the higher tier level;

in response to the performance breaching the higher performance threshold of the higher tier level, promoting the user-created content to an even higher tier level, the even higher tier level associated with an even higher performance threshold than the higher performance threshold;

training a machine learning model on example input-output pairs, each example input-output pair comprising a representation of the user-created content and the performance that breaches at least the predefined threshold;

determining, through the machine learning model, whether a popularity of the user-created content will grow exponentially;

predicting, through the machine learning model, performance statistics of the user-created content; and

causing display of the performance through a user interface.

20 . The system of claim 19 , wherein the performance is determined based on at least one of clicks, likes, comments, shares, views, or reads.