Managing content disclosure on social networking sites

• US 10,397,171 B2
• Filed: 03/31/2017
• Issued: 08/27/2019
• Est. Priority Date: 03/31/2017
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Hardee, Christopher J., Joroff, Steven R., Nesbitt, Pamela A., Schneider, Scott E.
• Primary Examiner(s): Mirza, Adnan M
• Application Number:
  US15/475,473
• Publication Number:
  US 20180287989A1
• Time in Patent Office: 879 Days
• CPC Class Codes: G06F 16/335 Filtering based on additional data, e.g. user or group profiles filtering in web context G06F16/9535, G06F16/9536
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

Embodiments include methods, systems and computer program products for managing content disclosure on social networking sites. In some embodiments, labels associated with a social network connection of a user can be associated with the social network connection based on the reactions of the social network connection to different social network posts. Content from a user device associated with the user can be received. The content can be processed to identify content types to associate with the content. A recommendation can be generated to share or not share the content with the social network connection based on the reactions to content posted by the user. An indication from the user to share the content with the social network connection can be received. The content can be shared with the social network connection based on the indication.

Claims

1. A computer-implemented method for managing content disclosure on social networking sites, the method comprising:

monitoring, using a processor, user-generated content and one or more social network connections of a user viewing or listening to the user-generated content, the one or more social network connections comprising a first connection and a second connection;

classifying, using the processor, the user-generated content into one or more content types;

determining, using the processor, a positive mood of the first connection based on a reaction of the first connection to the user-generated content and a negative mood of the second connection based on a reaction of the second connection to the user-generated content;

associating, using the processor, the first connection with a first label indicative of the positive mood and the second connection with a second label indicative of the negative mood;

receiving, using the processor, further user-generated content;

determining, using the processor, one or more further content types from the further user-generated content;

generating, using the processor, a recommendation to share the further content with the first connection, wherein the recommendation is based on the first label and the determined one or more further content types; and

generating, using the processor, a recommendation to not share the further content with the second connection, wherein the recommendation is based on the second label and the determined one or more further content types.

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

receiving, using the processor, an indication from the user of a preference to share or not share the further content with the one or more social network connections; and

sharing or not sharing, by the processor, the further content with the social network connection based on the indication.

3. The computer-implemented method of claim 1, wherein the recommendation to share the further content is based on a positive reaction or a neutral reaction by the first connection.

4. The computer-implemented method of claim 3, wherein the positive reaction is at least one of the following:

“

liking”

the content, an emoticon indicating a positive reaction to the content, a comment indicating a positive reaction to the content, sharing the content, and re-tweeting content.

5. The computer-implemented method of claim 1, wherein the recommendation to not share the further content is based on a negative reaction by the second connection.

6. The computer-implemented method of claim 5, wherein the negative reaction is at least one of the following:

an emoticon indicating a negative reaction to the content, and a comment indicating a negative reaction to the content.

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

associating the content with a confidence level based at least in part on the labels associated with the one or more social network connections and one or more content types associated with the content, wherein the confidence level is a numeric value indicative of a likelihood that the content is accurately categorized; and

determining to disclose the further content to the social network connection based on the confidence level of the content.

8. The computer-implemented method of claim 1, wherein classifying the user-generated content into one or more content types or determining one or more content types from the further user-generated content further comprises:

applying natural language processing or image analysis to the content or further content.

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

receiving a reaction to shared further content from the one or more social network connections; and

adding or removing one or more labels associated with the one or more social network connections based on the reaction to the content types associated with the further content.

10. A computer program product, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions readable by a processing circuit to cause the processing circuit to perform a method comprising:

monitoring user-generated content and one or more social network connections of a user viewing or listening to the user-generated content, the one or more social network connections comprising a first connection and a second connection;

classifying the user-generated content into one or more content types;

determining a positive mood of the first connection based on a reaction of the first connection to the user-generated content and a negative mood of the second connection based on a reaction of the second connection to the user-generated content;

associating the first connection with a first label indicative of the positive mood and the second connection with a second label indicative of the negative mood;

receiving further user-generated content;

determining one or more further content types from the further user-generated content;

generating a recommendation to share the further content with the first connection, wherein the recommendation is based on the first label and the determined one or more further content types; and

generating a recommendation to not share the further content with the second connection, wherein the recommendation is based on the second label and the determined one or more further content types.

11. The computer program product of claim 10 further comprising:

receiving an indication from the user of a preference to share or not share the further content with the one or more social network connections; and

sharing or not sharing the further content with the social network connection based on the indication.

12. The computer program product of claim 10, wherein a recommendation to share the further content is based on a positive reaction or a neutral reaction by the first connection.

13. The computer program product of claim 12, wherein the positive reaction is at least one of the following:

“

liking”

the content, an emoticon indicating a positive reaction to the content, a comment indicating a positive reaction to the content, sharing the content, and re-tweeting content.

14. The computer program product of claim 10, wherein a recommendation to not share the further content is based on a negative reaction by the second connection.

15. The computer program product of claim 14, wherein the negative reaction is at least one of the following:

an emoticon indicating a negative reaction to the content, and a comment indicating a negative reaction to the content.

16. The computer program product of claim 10, wherein classifying the user-generated content into one or more content types or determining one or more content types from the further user-generated content further comprises:

applying natural language processing or image analysis to the content or further content.

17. The computer program product of claim 10, further comprising:

receiving a reaction to the shared further content from the one or more social network connections; and

adding or removing one or more labels associated with the one or more social network connections based on the reaction to the content types associated with the further content.

18. The computer program product of claim 17 further comprising generating a further recommendation based on the one or more added or removed labels.

19. A computer system, comprising:

a processor in communication with one or more types of memory, the processor configured to;

monitor user-generated content and one or more social network connections of a user viewing or listening to the user-generated content, the one or more social network connections comprising a first connection and a second connection;

classify the user-generated content into one or more content types;

determine a positive mood of the first connection based on a reaction of the first connection to the user-generated content and a negative mood of the second connection based on a reaction of the second connection to the user-generated content;

associate the first connection with a first label indicative of the positive mood and the second connection with a second label indicative of the negative mood;

receive further user-generated content;

determine one or more further content types from the further user-generated content;

generate a recommendation to share the further content with the first connection, wherein the recommendation is based on the first label and the determined one or more further content types; and

generate a recommendation to not share the further content with the second connection, wherein the recommendation is based on the second label and the determined one or more further content types.

20. The system of claim 19, wherein the processor is further configured to:

receive an indication from the user of a preference to share the further content with the one or more social network connections; and

share or not share the further content with the social network connection based on the indication.