Content resonance

• US 9,524,321 B2
• Filed: 02/16/2016
• Issued: 12/20/2016
• Est. Priority Date: 03/31/2011
• Status: Active Grant

• Current Assignee: X Corp. (f/k/a Twitter, Inc.) (X Holdings Corp.)
• Original Assignee: X Corp. (f/k/a Twitter, Inc.) (X Holdings Corp.)
• Inventors: Goel, Ashish, Rajgopal, Srinivasan, Srivastava, Utkarsh, Banerji, Anamitra
• Primary Examiner(s): Lu, Kuen
• Application Number:
  US15/045,154
• Publication Number:
  US 20160162494A1
• Time in Patent Office: 308 Days
• Field of Search: 707/732
• US Class Current: 1/1
• CPC Class Codes: G06F 16/219 Managing data history or versioning querying versioned data G06F16/2474; querying temporal data G06F16/2477
• CPC Class Codes: G06F 16/23 Updating
• CPC Class Codes: G06F 16/2322 using timestamps
• CPC Class Codes: G06F 16/24524 Access plan code generation and invalidation; Reuse of access plans
• CPC Class Codes: G06F 16/24545 Selectivity estimation or determination
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/285 Clustering or classification
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: H04L 51/04 Real-time or near real-time messaging, e.g. instant messaging [IM]
• CPC Class Codes: H04L 51/212 using filtering or selective blocking

Abstract

A real-time messaging platform and method is disclosed which classifies messages in accordance with a combination of user engagement events as modified to reflect the temporal structure of the user engagement events. A message can be assigned a metric based, for example, on a weighted combination of user engagement rates, decayed with time to reflect an intuition that recent interactions by one or more users with the message will have a greater impact than older interactions with the message. Different types of interaction by one or more users with the message can be assigned different weights when the different engagement events are combined and, also, can be assigned different temporal characteristics.

Claims

1. A method of classifying messages in a real-time messaging platform comprising:

receiving engagement events associated with a message broadcast by a real-time messaging platform to a plurality of computing devices associated with users of the platform, the message authored by one of the users of the platform, each engagement event transmitted from one of the computing devices to the platform and indicating an interaction between the associated user and the message, each engagement event including a time when the interaction occurred;

generating a combination of the engagement events, the combination comprising weights for each of a plurality of different engagement types based on the engagement events and their associated times of interaction;

analyzing the combination of engagement events to detect an uncertainty associated with a number of received engagement events;

responsive to detecting the uncertainty, identifying engagement events of a similar engagement type, wherein an engagement type is determined based on a criterion associated with content of the engagement event;

updating the combination of engagement events by adjusting the weight of each different engagement type based on the identified similar engagement types; and

classifying the message at least in part based on the updated combination, wherein classifying the message includes determining a relevance of the message to a user of the real-time messaging platform based on the classification of the message.

2. The method of claim 1, wherein determining an uncertainty further comprises comparing a total number of received engagement events to a threshold number of engagement events.

3. The method of claim 1, wherein the criterion includes matching a keyword associated with the engagement event.

4. The method of claim 1, wherein the combination of engagement events is decayed exponentially based on a difference between a first time associated with a previous engagement event and a current time.

5. The method of claim 1, wherein generating the combination of the engagement events comprises:

identifying from the engagement events one or more positive interaction events that each indicates that the interaction between the user and the message is an action taken by the user on the message; and

identifying from the engagement events one or more impression events that each indicates that the interaction between the user and the message is a viewing of the message by the user.

6. The method of claim 5, further comprising:

generating a positive interaction total based on a total number of the one or more positive interaction events;

generating an impression total based on a total number of the one or more impression events; and

combining with different weights the positive interaction total and the impression total to generate the combination of the engagement events.

7. The method of claim 6, wherein generating the positive interaction total comprises:

identifying an interaction type for each of the one or more positive interaction events based on the interaction indicated by the positive interaction event, each interaction type associated with a different weight depending on a level of interaction indicated by the interaction type;

weighting each of the one or more positive interaction events based on the weight associated with the interaction type for the positive interaction event; and

combining the weighted one or more positive interaction events to generate the positive interaction total.

8. A non-transitory computer readable storage medium storing instructions for classifying messages in a real-time messaging platform, the instructions when executed by a processor cause the processor to:

receive engagement events associated with a message broadcast by a real-time messaging platform to a plurality of computing devices associated with users of the platform, the message authored by one of the users of the platform, each engagement event transmitted from one of the computing devices to the platform and indicating an interaction between the associated user and the message, each engagement event including a time when the interaction occurred;

generate a combination of the engagement events, the combination comprising weights for each of a plurality of different engagement types based on the engagement events and their associated times of interaction;

analyze the combination of engagement events to detect an uncertainty associated with a number of received engagement events;

in response to detecting the uncertainty, identify engagement events of a similar engagement type, wherein an engagement type is determined based on a criterion associated with content of the engagement event;

update the combination of engagement events by adjusting the weight of each different engagement type based on the identified similar engagement types; and

classify the message at least in part based on the updated combination, wherein classifying the message includes determining a relevance of the message to a user of the real-time messaging platform based on the classification of the message.

9. The computer readable storage medium of claim 8, wherein determining an uncertainty further comprises comparing a total number of received engagement events to a threshold number of engagement events.

10. The computer readable storage medium of claim 8, wherein the criterion includes matching a keyword associated with the engagement event.

11. The computer readable storage medium of claim 8, wherein the combination of engagement events is decayed exponentially based on a difference between a first time associated with a previous engagement event and a current time.

12. The computer readable storage medium of claim 8, wherein generating the combination of the engagement events comprises:

identifying from the engagement events one or more positive interaction events that each indicates that the interaction between the user and the message is an action taken by the user on the message; and

identifying from the engagement events one or more impression events that each indicates that the interaction between the user and the message is a viewing of the message by the user.

13. The computer readable storage medium of claim 12, further comprising:

generating a positive interaction total based on a total number of the one or more positive interaction events;

generating an impression total based on a total number of the one or more impression events; and

combining with different weights the positive interaction total and the impression total to generate the combination of the engagement events.

14. The computer readable storage medium of claim 13, wherein generating the positive interaction total comprises:

identifying an interaction type for each of the one or more positive interaction events based on the interaction indicated by the positive interaction event, each interaction type associated with a different weight depending on a level of interaction indicated by the interaction type;

weighting each of the one or more positive interaction events based on the weight associated with the interaction type for the positive interaction event; and

combining the weighted one or more positive interaction events to generate the positive interaction total.

15. A computer system for classifying messages in a real-time messaging platform, the computer system comprising a processor and a computer readable medium, the computer readable medium including computer program code for:

receiving engagement events associated with a message broadcast by a real-time messaging platform to a plurality of computing devices associated with users of the platform, the message authored by one of the users of the platform, each engagement event transmitted from one of the computing devices to the platform and indicating an interaction between the associated user and the message, each engagement event including a time when the interaction occurred;

generating a combination of the engagement events, the combination comprising weights for each of a plurality of different engagement types based on the engagement events and their associated times of interaction;

analyzing the combination of engagement events to detect an uncertainty associated with a number of received engagement events;

responsive to detecting the uncertainty, identifying engagement events of a similar engagement type, wherein an engagement type is determined based on a criterion associated with content of the engagement event;

updating the combination of engagement events by adjusting the weight of each different engagement type based on the identified similar engagement types; and

classifying the message at least in part based on the updated combination, wherein classifying the message includes determining a relevance of the message to a user of the real-time messaging platform based on the classification of the message.

16. The computer system of claim 15, wherein determining an uncertainty further comprises comparing a total number of received engagement events to a threshold number of engagement events.

17. The computer system of claim 15, wherein the criterion includes matching a keyword associated with the engagement event.

18. The computer system of claim 15, wherein the combination of engagement events is decayed exponentially based on a difference between a first time associated with a previous engagement event and a current time.

19. The computer system of claim 15, wherein generating the combination of the engagement events comprises:

identifying from the engagement events one or more positive interaction events that each indicates that the interaction between the user and the message is an action taken by the user on the message; and

identifying from the engagement events one or more impression events that each indicates that the interaction between the user and the message is a viewing of the message by the user.

20. The computer system of claim 19, further comprising:

generating a positive interaction total based on a total number of the one or more positive interaction events;

generating an impression total based on a total number of the one or more impression events; and

combining with different weights the positive interaction total and the impression total to generate the combination of the engagement events.

21. The computer system of claim 20, wherein generating the positive interaction total comprises:

identifying an interaction type for each of the one or more positive interaction events based on the interaction indicated by the positive interaction event, each interaction type associated with a different weight depending on a level of interaction indicated by the interaction type;

weighting each of the one or more positive interaction events based on the weight associated with the interaction type for the positive interaction event; and

combining the weighted one or more positive interaction events to generate the positive interaction total.