Discovering and ranking trending links about topics

• US 8,990,097 B2
• Filed: 07/05/2013
• Issued: 03/24/2015
• Est. Priority Date: 07/31/2012
• Status: Active Grant

• Current Assignee: Blazent Incorporated (ServiceNow Incorporated)
• Original Assignee: Bottlenose, Inc. (ServiceNow Incorporated)
• Inventors: Spivack, Nova, Heide, Dominiek ter
• Primary Examiner(s): OUELLETTE, JONATHAN P
• Application Number:
  US13/936,072
• Publication Number:
  US 20140040387A1
• Time in Patent Office: 627 Days
• Field of Search: 705 11-912
• US Class Current: 705/1.1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: H04L 51/52 for supporting social networking services

Abstract

A system and a method for discovering and ranking trending links about topics are presented. The method comprises steps of receiving a plurality of messages from a social networking server, identifying a plurality of trending objects from the plurality of messages, generating at least one trending score for each trending object of the trending objects, and presenting a list of the trending objects based on the trending scores.

Claims

1. A method for enabling a machine to detect and analyze trends within a data stream received by the machine, the method comprising:

receiving, at a social intelligence system, a query from a user;

receiving, at the social intelligence system, a plurality of messages from a social networking server;

identifying, using a processor of the system, a plurality of trending objects from the plurality of messages;

generating, using the processor, a momentum score for each of the plurality of trending objects,

wherein the momentum score is calculated based on a boost factor that;

(i) exponentially decreases from a maximum boost value to a value of one (1) in a predetermined period of time starting from when an associated message is received, and

(ii) continues to exponentially decrease until the associated message expires;

generating, using the processor, an importance score for each of the plurality of messages, wherein the importance score is calculated based on a multiplication product of (i) the momentum scores of all the identified trending objects that correspond to a given message, (ii) a total number of available links to the given message, (iii) a first relevancy between content of the given message and the query, (iv) a second relevancy between the trending objects that correspond to the given message and an interest profile of the user, and (v) a number of subscribers who follow an author of the given message; and

ranking the plurality of trending objects based on their momentum scores and their associated messages'"'"' importance scores.

2. The method of claim 1, wherein the momentum score further depends on a number of times that the trending object appears in the plurality of messages.

3. The method of claim 1, wherein the momentum score further depends on a time period since a latest message of the plurality of messages mentioned the trending object.

4. The method of claim 1, wherein the momentum score further depends on a number of followers of a particular user or a set of users who mentioned the trending object in a message of the plurality of messages, or where it depends on a number of participants in a particular conversation or who pay attention to a particular topic or set of topics, irrespective of whether or not they follow any particular user or a set of users who mentioned the trending object in a message of the plurality of messages.

5. The method of claim 1, wherein the momentum score further depends on an influence profile of a user Who mentioned the trending object in a message of the plurality of messages.

6. The method of claim 1, wherein the momentum score further depends on whether the plurality of messages includes the trending object or information related to the trending object.

7. The method of claim 1, wherein the momentum score of the trending object equals M, wherein the maximum boost value equals maxBoost, and wherein M=maxBoost/message age^(Log(maxBoost)/Log(the predetermined period of time)).

8. The method of claim 1, wherein the trending objects include topics, types, hashtags, people, messages, or links.

9. The method of claim 1, further comprising:

presenting the trending objects as scatters or paths in a quadrant scatter plot, wherein a volume of each scatter indicates a momentum score of a trending object represented by the scatter.

10. The method of claim 1, wherein said identifying includes:

identifying a plurality of trending objects from the plurality of messages using a speech tagging process.

11. The method of claim 1, wherein the momentum score further depends on a mass factor, a recency factor, and a momentum factor, the mass factor indicating how often the trending object occurred in the plurality of messages, the recency factor indicating how recent the trending object appeared in the plurality of messages, and the momentum factor indicating how fast the trending object gained its momentum score in the plurality of messages.

12. The method of claim 1, wherein the plurality of messages contains repast directives and hashtags.

13. The method of claim 1, further comprising:

generating, using the processor, a co-occurrence score for a first trending object relative to a second trending object, the co-occurrence score is calculated based on a number of messages that mention both of the first and the second trending objects; and

associating, using the processor, the first and the second trending objects if the co-occurrence score exceeds a predetermined value.

14. The method of claim 13, further comprising:

treating the two associated trending objects as a single trending object for predicting trends based on the plurality of messages.

15. The method of claim 13, Wherein the importance scores are normalized to a predetermined scale.

16. The method of claim 13, wherein the importance scores are generated irrespective of any specific user of the social networking server.

17. The method of claim 13, wherein the trending objects represent a plurality of objects in a category.

18. A system configured for detecting and analyzing trends within a data stream received by the system, the system comprising:

a network component configured to receive a query from a user and to receive a plurality of messages from a social networking server;

a processor; and

a memory storing instructions which, when executed by the processor, cause the system to perform a process including;

identifying a plurality of trending objects from the plurality of messages;

generating a momentum score for each of the plurality of trending objects

wherein the momentum score is calculated based on a boost factor that;

(i) exponentially decreases from a maximum boost value to a value of one (1) in a predetermined period of time starting from when as associated message is received, and

(ii) continues to exponentially decrease until the associated message expires;

generating, using the processor, an importance score for each of the plurality of messages, wherein the importance score is calculated based on a multiplication product of (i) the momentum scores of all the identified trending objects that correspond to a given message, (ii) a total number of available links to the given message, (iii) a first relevancy between content of the given message and the query, (iv) a second relevancy between the trending objects that correspond to the given message and an interest profile of the user, and (v) a number of subscribers who follow an author of the given message; and

ranking the plurality of trending objects based on their momentum scores and their associated messages'"'"' importance scores.

19. The system of claim 18, further comprising:

an output component configured to present a list of the plurality of messages based on their importance scores.

20. The system of claim 18, further comprising:

an output component configured to present the trending objects as scatters in a quadrant scatter plot, wherein a volume of each scatter indicates a momentum score of a trending object represented by the scatter.

21. The system of claim 18, wherein the momentum score for each trending object is further calculated based on a number of times that the trending object appears in the plurality of messages.

22. The system of claim 18, wherein the momentum score equals M, wherein the maximum boost value equals maxBoost, and wherein M=maxBoost/message age^(Log(maxBoost)/Log(the predetermined period of time)).

23. The system of claim 18, wherein the momentum score for each trending object includes a link score, the link score depends on one or more of factors including:

a number of times that the trending object appear in the plurality of messages,

a time period since a latest message of the plurality of messages mentioned the trending object,

a predetermined boost factor,

a number of followers of a particular user or a set of users who mentioned the trending object in a message of the plurality of messages, and

an influence profile of a user who mentioned the trending object in a message of the plurality of messages.

24. A method for enabling a machine to detect and analyze trends within a data stream received by the machine, the method comprising:

receiving, at a social intelligence system, a query from a user;

receiving, at the social intelligence system, a plurality of messages from a social networking server;

identifying, using a processor of the system, a plurality of trending objects from the plurality of messages;

generating, using the processor, a momentum score for each of the plurality of trending objects, wherein the momentum score is calculated based on a boost factor that;

(i) exponentially decreases from a maximum boost value to a value of one (1) in a predetermined period of time starting from when an associated message is received, and (ii) continues to exponentially decrease until the associated message expires;

generating, using the processor, an importance score for each of the plurality of messages, wherein the importance score is calculated based on a multiplication product of (i) the momentum scores of all the identified trending objects that correspond to a given message, (ii) a total number of available links to the given message, (iii) a first relevancy between content of the given message and the query, (iv) a second relevancy between the trending objects that correspond to the given message and an interest profile of the user, and (v) a number of subscribers who follow an author of the given message;

ranking the plurality of trending objects based on their momentum scores and their associated messages'"'"' importance scores;

generating, using the processor, a co-occurrence score for a first trending object relative to a second trending object, the co-occurrence score is calculated based on a number of messages that mention both of the first and the second trending objects;

associating, using the processor, the first and the second trending objects if the co-occurrence score exceeds a predetermined value; and

updating the first and the second trending objects'"'"' importance scores based on the co-occurrence score after associating the first and the second trending objects.