SYSTEMS AND METHODS FOR MANAGING CONTENT
Systems, methods, and non-transitory computer readable media are configured to determine a likelihood of a user choosing to reveal a given content item when contents of the content item are obscured. The likelihood can be determined based at least in part on a trained machine learning model. An extent by which to obscure the content item based at least in part on the likelihood can be determined. Subsequently, an obscured version of the content item can be provided for display. The content item can be obscured based at least in part on the determined extent.
1 . A computer-implemented method comprising:
determining, by a computing system, a likelihood of a user choosing to reveal a given content item when contents of the content item are obscured, the likelihood being determined based at least in part on a trained machine learning model;
determining, by the computing system, an extent by which to obscure the content item based at least in part on the likelihood; and
providing, by the computing system, an obscured version of the content item for display, wherein the content item is obscured based at least in part on the determined extent.
2 . The computer-implemented method of claim 1 , further comprising:
providing, by the computing system, to the trained machine learning model, content item feature data, wherein the content item feature data comprises one or more of a sensitive content category or a sensitive content score.
3 . The computer-implemented method of claim 1 , further comprising:
providing, by the computing system, to the trained machine learning model, user feature data.
4 . The computer-implemented method of claim 1 , further comprising:
generating, by the computing system, the obscured version of the content item, wherein generating the obscured version of the content item comprises one or more of:
superimposing, by the computing system, a block of color, wherein the determined extent corresponds to an opacity of the block of color; or
applying, by the computing system, a blur effect, wherein the determined extent corresponds to an intensity of the blur.
5 . The computer-implemented method of claim 1 , further comprising:
applying, by the computing system, a text overlay, wherein the text overlay provides a sensitive content warning.
6 . The computer-implemented method of claim 1 , wherein the obscured version of the content item is presented via one or more of a profile, a feed, or a single content item display.
7 . The computer-implemented method of claim 1 , further comprising:
receiving, by the computing system, an exposure indication; and
retraining, by the computing system, based on the exposure indication, the trained machine learning model.
8 . The computer-implemented method of claim 7 , further comprising:
complying, by the computing system, with the exposure indication.
9 . The computer-implemented method of claim 1 , further comprising:
providing, by a computing system, to a second trained machine learning model, a content item representation; and
receiving, by the computing system, from the second trained machine learning model, one or more of a sensitive content category or a sensitive content score.
10 . The computer-implemented method of claim 9 , wherein the content item representation comprises one or more concepts.
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 likelihood of a user choosing to reveal a given content item when contents of the content item are obscured, the likelihood being determined based at least in part on a trained machine learning model;
determining an extent by which to obscure the content item based at least in part on the likelihood; and
providing an obscured version of the content item for display, wherein the content item is obscured based at least in part on the determined extent.
12 . The system of claim 11 , wherein the instructions, when executed by the at least one processor, further cause the system to perform:
providing to the trained machine learning model, content item feature data, wherein the content item feature data comprises one or more of a sensitive content category or a sensitive content score.
13 . The system of claim 11 , wherein the instructions, when executed by the at least one processor, further cause the system to perform:
providing to the trained machine learning model, user feature data.
14 . The system of claim 11 , wherein the instructions, when executed by the at least one processor, further cause the system to perform:
generating the obscured version of the content item, wherein generating the obscured version of the content item comprises one or more of:
superimposing a block of color, wherein the determined extent corresponds to an opacity of the block of color; or
applying a blur effect, wherein the determined extent corresponds to an intensity of the blur.
15 . The system of claim 11 , wherein the instructions, when executed by the at least one processor, further cause the system to perform:
receiving an exposure indication; and
retraining based on the exposure indication, the trained machine learning model.
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 likelihood of a user choosing to reveal a given content item when contents of the content item are obscured, the likelihood being determined based at least in part on a trained machine learning model;
determining an extent by which to obscure the content item based at least in part on the likelihood; and
providing an obscured version of the content item for display, wherein the content item is obscured based at least in part on the determined extent.
17 . The non-transitory computer-readable storage medium of claim 16 , wherein the instructions, when executed by the at least one processor of the computing system, further cause the computing system to perform:
providing to the trained machine learning model, content item feature data, wherein the content item feature data comprises one or more of a sensitive content category or a sensitive content score.
18 . The non-transitory computer-readable storage medium of claim 16 , wherein the instructions, when executed by the at least one processor of the computing system, further cause the computing system to perform:
providing to the trained machine learning model, user feature data.
19 . The non-transitory computer-readable storage medium of claim 16 , wherein the instructions, when executed by the at least one processor of the computing system, further cause the computing system to perform:
generating the obscured version of the content item, wherein generating the obscured version of the content item comprises one or more of:
superimposing a block of color, wherein the determined extent corresponds to an opacity of the block of color; or
applying a blur effect, wherein the determined extent corresponds to an intensity of the blur.
20 . The non-transitory computer-readable storage medium of claim 16 , wherein the instructions, when executed by the at least one processor of the computing system, further cause the computing system to perform:
receiving an exposure indication; and
retraining based on the exposure indication, the trained machine learning model.