Systems, methods, and non-transitory computer-readable media can acquire a set of individual time series associated with a set of users. Each of the individual time series can be associated with a respective user out of the set of the users. A plurality of variables represented via the set of individual time series can be selected. The plurality of variables can include at least a first variable and a second variable. One or more regression techniques can be applied to at least the first variable and the second variable. A set of sensitivity metrics for the set of users can be determined based on the one or more regression techniques. A respective sensitivity metric out of the set of sensitivity metrics can be determined for each of the users.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: acquiring, by a computing system, a set of individual time series associated with a set of users, each of the individual time series being associated with a respective user out of the set of the users; selecting, by the computing system, a plurality of variables represented via the set of individual time series, the plurality of variables including at least a first variable and a second variable; applying, by the computing system, one or more regression techniques to at least the first variable and the second variable; and determining, by the computing system, based on the one or more regression techniques, a set of sensitivity metrics for the set of users, a respective sensitivity metric out of the set of sensitivity metrics being determined for each of the users. 2. The computer-implemented method of claim 1, further comprising: acquiring a set of feature values for a set of features associated with the set of users; applying machine learning to the set of feature values and the set of sensitivity metrics to develop a sensitivity model; and identifying, based on the sensitivity model, one or more target users. 3. The computer-implemented method of claim 2, wherein each target user out of the one or more target users is identified based on a respective set of particular feature values associated with each target user. 4. The computer-implemented method of claim 2, wherein the sensitivity model is indicative of one or more correlations between the set of features and the set of sensitivity metrics. 5. The computer-implemented method of claim 2, wherein the set of features is associated with at least one of a social networking utilization feature, a page inventory feature, a page like feature, a page access amount feature, a location feature, or a device feature. 6. The computer-implemented method of claim 2, further comprising: applying one or more social networking policies to the one or more target users. 7. The computer-implemented method of claim 6, wherein the one or more social networking policies are associated with at least one of receiving page suggestions, ranking feed content, interacting with posts, generating posts, interacting with advertisements, or developing social connections. 8. The computer-implemented method of claim 1, wherein each of the individual time series associated with the respective user out of the set of the users includes social networking behavioral data that is personalized for the respective user over a specified moving time frame. 9. The computer-implemented method of claim 1, wherein each sensitivity metric out of the set of sensitivity metrics corresponds to a respective regression coefficient, for each user out of the set of users, that represents one or more correlations between at least the first variable and the second variable. 10. The computer-implemented method of claim 1, wherein the one or more regression techniques include one or more linear regression processes. 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: acquiring a set of individual time series associated with a set of users, each of the individual time series being associated with a respective user out of the set of the users; selecting a plurality of variables represented via the set of individual time series, the plurality of variables including at least a first variable and a second variable; applying one or more regression techniques to at least the first variable and the second variable; and determining, based on the one or more regression techniques, a set of sensitivity metrics for the set of users, a respective sensitivity metric out of the set of sensitivity metrics being determined for each of the users. 12. The system of claim 11, wherein the instructions cause the system to further perform: acquiring a set of feature values for a set of features associated with the set of users; applying machine learning to the set of feature values and the set of sensitivity metrics to develop a sensitivity model; and identifying, based on the sensitivity model, one or more target users. 13. The system of claim 12, wherein the instructions cause the system to further perform: applying one or more social networking policies to the one or more target users. 14. The system of claim 11, wherein each of the individual time series associated with the respective user out of the set of the users includes social networking behavioral data that is personalized for the respective user over a specified moving time frame. 15. The system of claim 11, wherein each sensitivity metric out of the set of sensitivity metrics corresponds to a respective regression coefficient, for each user out of the set of users, that represents one or more correlations between at least the first variable and the second variable. 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: acquiring a set of individual time series associated with a set of users, each of the individual time series being associated with a respective user out of the set of the users; selecting a plurality of variables represented via the set of individual time series, the plurality of variables including at least a first variable and a second variable; applying one or more regression techniques to at least the first variable and the second variable; and determining, based on the one or more regression techniques, a set of sensitivity metrics for the set of users, a respective sensitivity metric out of the set of sensitivity metrics being determined for each of the users. 17. The non-transitory computer-readable storage medium of claim 16, wherein the instructions cause the computing system to further perform: acquiring a set of feature values for a set of features associated with the set of users; applying machine learning to the set of feature values and the set of sensitivity metrics to develop a sensitivity model; and identifying, based on the sensitivity model, one or more target users. 18. The non-transitory computer-readable storage medium of claim 17, wherein the instructions cause the computing system to further perform: applying one or more social networking policies to the one or more target users. 19. The non-transitory computer-readable storage medium of claim 16, wherein each of the individual time series associated with the respective user out of the set of the users includes social networking behavioral data that is personalized for the respective user over a specified moving time frame. 20. The non-transitory computer-readable storage medium of claim 16, wherein each sensitivity metric out of the set of sensitivity metrics corresponds to a respective regression coefficient, for each user out of the set of users, that represents one or more correlations between at least the first variable and the second variable.