SYSTEMS AND METHODS FOR ANALYZING INTERACTION-BAIT CONTENT BASED ON CLASSIFIER MODELS

1 . A computer-implemented method comprising:

selecting, by a computing system, one or more content items that are associated with one or more interactions that each at least meet a specified interaction metric threshold;

acquiring, by the computing system, data associated with the one or more content items;

developing, by the computing system, a classifier based on the data associated with the one or more content items; and

identifying, by the computing system, based on the classifier, at least some of the one or more content items as having at least a threshold confidence score of being interaction-bait content.

2 . The computer-implemented method of claim 1 , wherein developing the classifier further comprises:

identifying, based on the data associated with the one or more content items, at least some of the one or more content items that have been labeled as corresponding to interaction-bait content;

acquiring, based on the data associated with the one or more content items, a set of feature values associated with the at least some of the one or more content items; and

utilizing, at least in part, the set of feature values to apply machine learning to train the classifier.

3 . The computer-implemented method of claim 1 , wherein a particular set of feature values associated with a particular content item out of the one or more content items is acquired based on the data associated with the one or more content items, wherein the particular set of feature values indicates one or more specified keywords, and wherein the one or more specified keywords indicated via the particular set of feature values cause an increase to a confidence score representing whether the particular content item corresponds to interaction-bait content.

4 . The computer-implemented method of claim 1 , wherein the one or more interactions include at least one of a click, a vote, a comment, a share, or a save.

5 . The computer-implemented method of claim 1 , wherein the specified interaction metric threshold includes at least one of: 1) a specified minimum quantity of occurrences for a particular interaction or 2) a specified minimum content popularity ranking with respect to a certain interaction.

6 . The computer-implemented method of claim 1 , wherein the data associated with the one or more content items includes at least one of a label, a summary, a description, a caption, a tag, a classification, a location, a web address, a recognized object, or recognized text.

7 . The computer-implemented method of claim 6 , wherein the label is provided based on manual effort.

8 . The computer-implemented method of claim 6 , wherein the label is provided for a particular content item out of the one or more content items, wherein the label indicates whether the particular content item corresponds to interaction-bait content, and wherein the label is utilized for developing the classifier.

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

rectifying one or more influence metrics associated with the at least some of the one or more content items.

10 . The computer-implemented method of claim 9 , wherein the one or more influence metrics includes at least one of: 1) a ranking metric associated with a feed ranking of a particular content item out of the at least some of the one or more content items or 2) a relevancy metric associated with a calculated confidence score representing whether the particular content item is relevant with respect to a particular content accessing user.

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:

selecting one or more content items that are associated with one or more interactions that each at least meet a specified interaction metric threshold;

acquiring data associated with the one or more content items;

developing a classifier based on the data associated with the one or more content items; and

identifying, based on the classifier, at least some of the one or more content items as having at least a threshold confidence score of being interaction-bait content.

12 . The system of claim 11 , wherein developing the classifier further comprises:

identifying, based on the data associated with the one or more content items, at least some of the one or more content items that have been labeled as corresponding to interaction-bait content;

acquiring, based on the data associated with the one or more content items, a set of feature values associated with the at least some of the one or more content items; and

utilizing, at least in part, the set of feature values to apply machine learning to train the classifier.

13 . The system of claim 11 , wherein the data associated with the one or more content items includes at least one of a label, a summary, a description, a caption, a tag, a classification, a location, a web address, a recognized object, or recognized text.

14 . The system of claim 11 , wherein the instructions cause the system to further perform:

rectifying one or more influence metrics associated with the at least some of the one or more content items.

15 . The system of claim 14 , wherein the one or more influence metrics includes at least one of: 1) a ranking metric associated with a feed ranking of a particular content item out of the at least some of the one or more content items or 2) a relevancy metric associated with a calculated confidence score representing whether the particular content item is relevant with respect to a particular content accessing user.

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:

selecting one or more content items that are associated with one or more interactions that each at least meet a specified interaction metric threshold;

acquiring data associated with the one or more content items;

developing a classifier based on the data associated with the one or more content items; and

identifying, based on the classifier, at least some of the one or more content items as having at least a threshold confidence score of being interaction-bait content.

17 . The non-transitory computer-readable storage medium of claim 16 , wherein developing the classifier further comprises:

identifying, based on the data associated with the one or more content items, at least some of the one or more content items that have been labeled as corresponding to interaction-bait content;

acquiring, based on the data associated with the one or more content items, a set of feature values associated with the at least some of the one or more content items; and

utilizing, at least in part, the set of feature values to apply machine learning to train the classifier.

18 . The non-transitory computer-readable storage medium of claim 16 , wherein the data associated with the one or more content items includes at least one of a label, a summary, a description, a caption, a tag, a classification, a location, a web address, a recognized object, or recognized text.

19 . The non-transitory computer-readable storage medium of claim 16 , wherein the instructions cause the computing system to further perform:

rectifying one or more influence metrics associated with the at least some of the one or more content items.

20 . The non-transitory computer-readable storage medium of claim 19 , wherein the one or more influence metrics includes at least one of: 1) a ranking metric associated with a feed ranking of a particular content item out of the at least some of the one or more content items or 2) a relevancy metric associated with a calculated confidence score representing whether the particular content item is relevant with respect to a particular content accessing user.