DISCOVERING AND RANKING TRENDING LINKS ABOUT TOPICS

• US 20140040387A1
• Filed: 07/05/2013
• Published: 02/06/2014
• 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
• Granted Patent: US 8,990,097 B2
• Time in Patent Office: Days
• US Class Current: 709/206
• 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 comprising:

receiving, at a 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;

for each trending object of the trending objects, generating, using the processor, at least one trending score based on a momentum score for the trending object; and

presenting a list of the trending objects based on the trending scores,

wherein the momentum score of the trending object is calculated based on a boost factor which;

(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 trending object expires.

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

3. The method of claim 1, wherein the trending score 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 trending score 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 trending score depends on an interest profile, or 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 trending score 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 agê

(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, wherein said presenting includes:

presenting the trending objects as scatters or paths in a quadrant scatter plot, wherein a volume of each scatter indicates a trending 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 trending score depends on a mass factor, a recency factor, and a momentum factor, the mass factor indicates a number of times that the trending object occurred in the plurality of messages, the recency factor indicates how recent the trending object appeared in the plurality of messages, and the momentum factor indicates how fast the trending object gained trending score or other metrics (such as volume, acceleration, sentiment, etc.) in the plurality of messages.

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

13. A system comprising:

a network component configured 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;

for each trending object of the trending objects, generating at least one trending score based on a momentum score the trending object; and

presenting a list of the trending objects based on the trending scores,

wherein the momentum score of the trending object is calculated based on a boost factor which;

(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 trending object expires.

14. The system of claim 13, further comprising:

an output component configured to present the list of the trending objects based on the trending scores.

15. The system of claim 13, 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 trending score of a trending object represented by the scatter.

16. The system of claim 13, wherein the trending scores for each trending object include a number of times that the trending object appears in the plurality of messages.

17. The system of claim 13, wherein the momentum score equals M, wherein the maximum boost value equals maxBoost, and wherein M=maxBoost/message agê

(Log(maxBoost)/Log(the predetermined period of time)).

18. The system of claim 13, wherein the trending scores 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, 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, and

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

19. A method comprising:

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

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

for each trending object of the trending objects, generating, using the processor, at least one trending score based on a momentum score for the trending object, wherein the momentum score of the trending object is calculated based on a boost factor which;

(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 trending object expires;

generating, using the processor;

a co-occurrence score for the two trending objects, the co-occurrence score depends on a number of messages of the plurality of messages that mention both of the two trending objects; and

associating, using the processor, the two trending objects based on the co-occurrence score.

20. The method of claim 19, further comprising:

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

21. The method of claim 19, wherein the trending scores are normalized to a predetermined scale.

22. The method of claim 19, wherein the trending scores are generated with no respect to any specific user of the social networking server.

23. The method of claim 19, wherein the trending objects represent a plurality of objects in a category.

24. The method of claim 23, wherein the objects in the category include movies, TV shows, advertisements, online videos, celebrities, news articles, brands, products, or photos.