DETECTING EXTRANEOUS SOCIAL MEDIA MESSAGES

• US 20180176173A1
• Filed: 12/15/2016
• Published: 06/21/2018
• Est. Priority Date: 12/15/2016
• Status: Abandoned Application

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Keysers, Daniel Martin, Deselaers, Thomas, Carbune, Victor
• Application Number:
  US15/380,748
• Publication Number:
  US 20180176173A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 40/20 Natural language analysis semantic analysis of natural language G06F40/30
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: H04L 51/063 Content adaptation, e.g. replacement of unsuitable content
• CPC Class Codes: H04L 51/216 Handling conversation history, e.g. grouping of messages in sessions or threads
• CPC Class Codes: H04L 51/52 for supporting social networking services

Abstract

A social network server system may receive a social media message that is to be posted at the social network server system, the social media message being authored by a user of the social network server system. Prior to posting the social media message at the social network server system, the social network server system may determine, based at least in part on applying one or more rules to content of the social media message, a likelihood that the user would modify the content of the social media message after it is posted at the social network server system, wherein the one or more rules are generated based at least in part on previous actions taken by the user on previous social media messages authored by the user and posted at the social network server system and may, responsive to determining that the likelihood exceeds a threshold, generate an alert message.

Claims

1. A method comprising:

receiving, by a social network server system, a social media message that is to be posted at the social network server system, the social media message being authored by a user of the social network server system;

prior to posting the social media message at the social network server system;

determining, by the social network server system, and based at least in part on applying one or more rules to content of the social media message, a likelihood that the user would modify the content of the social media message after it is posted at the social network server system, wherein the one or more rules are generated based at least in part on previous actions taken by the user on previous social media messages authored by the user and posted at the social network server system; and

responsive to determining that the likelihood exceeds a threshold, generating, by the social network server system, an alert message.

2. The method of claim 1, wherein determining the likelihood that the user would modify the content of the social media message after it is posted at the social network server system comprises determining, by the social network server system, a likelihood that the user would delete the social media message from the social network server system after it is posted at the social network server system.

3. The method of claim 1, wherein the one or more rules are generated based at least in part on the previous social media messages authored by the user that were posted at the social network server system and then later modified by the user.

4. The method of claim 3, further comprising:

generating, by the social network server system, the one or more rules by machine training a model based at least in part on the previous social media messages authored by the user that were posted at the social networking server system and then later modified by the user.

5. The method of claim 4, wherein determining the likelihood that the user would modify the content of the social media message after it is posted at the social network server system comprises:

inputting, by the social network server system, the social media message into the model executing at the social network server system; and

receiving, by the social network server system, a score for the social media message that is output by the model, wherein the score corresponds to the likelihood that the user would modify the content of the social media message after it is posted at the social network server system.

6. The method of claim 1, wherein determining the likelihood that the user would modify the content of the social media message after it is posted at social network server system is based at least in part on one or more of:

textual content of the social media message,

contextual information associated with the social media message, and

an intended audience for the social media message.

7. The method of claim 1, wherein the one or more rules comprise first one or more rules, wherein determining the likelihood that the user would modify the content of the social media message after it is posted at the social network server system is further based at least in part on applying second one or more rules to the content of the social media message, and wherein the second one or more rules are generated based at least in part on previous actions taken by a plurality of users of the social network server system on previous social media messages authored by the plurality users and posted at the social network server system.

8. The method of claim 7, wherein the one or more rules are generated based at least in part on the previous social media messages authored by the plurality of users that were posted at the social network server system and then later modified by one or more of the plurality of users.

9. The method of claim 8, further comprising:

generating, by the social network server system, the second one or more rules by machine training a model based at least in part on the previous social media messages authored by the plurality of users that were posted at the social network server system and then later modified by one or more of the plurality of users.

10. The method of claim 9, wherein determining the likelihood that the user would modify the content of the social media message after it is posted at the social network server system comprises:

inputting, by the social network server system, the social media message into the model executing at the social network server system; and

receiving, by the social network server system, a score for the social media message that is output by the model, wherein the score corresponds to the likelihood that the user would modify the content of the social media message after it is posted at the social network server system.

11. The method of claim 1, wherein the one or more rules comprise first one or more rules, wherein determining the likelihood that the user would modify the content of the social media message after it is posted at the social network server system is further based at least in part on applying third one or more rules to the content of the social media message, wherein the third one or more rules are manually generated.

12. A social network server system, comprising:

a non-transitory computer-readable storage medium;

at least one processor communicatively coupled to the computer-readable storage medium, the at least one processor being configured to;

receive a social media message that is to be posted at the social network server system, the social media message being authored by a user of the social network server system;

prior to posting the social media message at the social network server system;

determine, based at least in part on applying one or more rules stored in the non-transitory computer-readable storage medium to content of the social media message, a likelihood that the user would modify the content of the social media message after it is posted at the social network server system, wherein the one or more rules are generated based at least in part on previous actions taken by the user on previous social media messages authored by the user and posted at the social network server system; and

responsive to determining that the likelihood exceeds a threshold, generate an alert message.

13. The social network server system of claim 12, wherein the one or more rules are generated based at least in part on the previous social media messages authored by the user that were posted at the social network server system and then later modified by the user.

14. The social network server system of claim 13, wherein the at least one processor is further configured to:

generate the one or more rules by machine training a model based at least in part on the previous social media messages authored by the user that were posted at the social networking server system and then later modified by the user.

15. The social network server system of claim 14, wherein the at least one processor is further configured to:

input the social media message into the model executing at the social network server system; and

receive a score for the social media message that is output by the model, wherein the score corresponds to the likelihood that the user would modify the content of the social media message after it is posted at the social network server system.

16. The social network server system of claim 12, wherein the one or more rules comprise a first one or more rules, and wherein the at least one processor is further configured to:

determine the likelihood that the user would modify the content of the social media message after it is posted at the social network server system based further at least in part on applying second one or more rules to the content of the social media message, wherein the second one or more rules are generated based at least in part on previous actions taken by a plurality of users of the social network server system on previous social media messages authored by the plurality users and posted at the social network server system.

17. A non-transitory computer readable medium encoded with instructions that, when executed, cause one or more processors of a social network server system to:

receive a social media message that is to be posted at the social network server system, the social media message being authored by a user of the social network server system; and

prior to posting the social media message at the social network server system;

determine, based at least in part on applying one or more rules to content of the social media message, a likelihood that the user would modify the content of the social media message after it is posted at the social network server system, wherein the one or more rules are generated based at least in part on previous actions taken by the user on previous social media messages authored by the user and posted at the social network server system; and

responsive to determining that the likelihood exceeds a threshold, generate an alert message.

18. The non-transitory computer readable medium of claim 17, wherein the one or more rules are generated based at least in part on the previous social media messages authored by the user that were posted at the social network server system and then later modified by the user.

19. The non-transitory computer readable medium of claim 18, wherein the instructions further cause the one or more processors to:

generate the one or more rules by machine training a model based at least in part on the previous social media messages authored by the user that were posted at the social networking server system and then later modified by the user.

20. The non-transitory computer readable medium of claim 19, wherein the instructions further cause the one or more processors to:

input the social media message into the model executing at the social networker server system; and

receive a score for the social media message that is output by the model, wherein the score corresponds to the likelihood that the user would modify the content of the social media message after it is posted at the social network server system.