Systems and methods for evaluating user activity

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 output forecasted measurements of at least one activity for at least a first group of users, wherein the at least one activity includes a number of likes performed by the first group of users, and wherein the machine learning model is trained using a set of training examples that each include respective counts of the at least one activity having been performed by each group of users in a set of groups over some period of time that precedes an event, the set of groups including the first group of users and a plurality of different groups of users;
• determining the event that may affect at least one activity being performed by a first group of users through the computing system;
• determining a set of first measurements of the at least one activity being performed by the first group of users over a first period of time;
• generating a set of second measurements of the at least one activity for the first group of users over a second period of time, wherein the set of second measurements are predicted based at least in part on the trained machine learning model, and wherein the set of second measurements for the first group of users are predicted based at least in part on the at least one activity having been performed by one or more groups of users in a set of groups, the set of groups not including the first group of users;
• and
• generating data describing an impact of the event on the first group of users based at least in part on the set of first measurements and the set of second measurements.

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 output forecasted measurements of at least one activity for at least a first group of users, wherein the at least one activity includes a number of likes performed by the first group of users, and wherein the machine learning model is trained using a set of training examples that each include respective counts of the at least one activity having been performed by each group of users in a set of groups over some period of time that precedes an event, the set of groups including the first group of users and a plurality of different groups of users;
• determining the event that may affect at least one activity being performed by a first group of users through the computing system;
• determining a set of first measurements of the at least one activity being performed by the first group of users over a first period of time;
• generating a set of second measurements of the at least one activity for the first group of users over a second period of time, wherein the set of second measurements are predicted based at least in part on the trained machine learning model, and wherein the set of second measurements for the first group of users are predicted based at least in part on the at least one activity having been performed by one or more groups of users in a set of groups, the set of groups not including the first group of users;
• and
• generating data describing an impact of the event on the first group of users based at least in part on the set of first measurements and the set of second measurements.