Method and system for determining on-line influence in social media

• US 9,245,252 B2
• Filed: 05/07/2009
• Issued: 01/26/2016
• Est. Priority Date: 05/07/2008
• Status: Active Grant

• Current Assignee: Salesforce.com, Inc.
• Original Assignee: Salesforce.com, Inc.
• Inventors: Newton, Christopher Daniel, Lebrun, Marcel Albert, Ramsey, Christopher Bennett
• Primary Examiner(s): YU, ARIEL J
• Application Number:
  US12/437,418
• Publication Number:
  US 20090281851A1
• Time in Patent Office: 2,455 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A method and system for determining on-line influence in social media is disclosed. A recursive site influence modeling module computes a site influence from aggregated viral properties of content hosted by the site and further integrates, in the formulation of the site influence model, the influence of commentors, commenting on the hosted content, and the influence of individuals cited in the content. The influence of the commentors and individuals is calculated by aggregating viral properties of related content and as well by taking into account the influence of outlets owned by the commentors and the individuals.

Claims

1. A computer-implemented method performed by a computer for determining a topical influence value of a commentor, an influence value of an individual and a topical influence value of a web-site, wherein the computer is in communication with a server via network to access a web-site hosted by the server, the computer-implemented method comprising steps of:

matching and tagging content within the web-site with a selected topic, using a processor of the computer, to generate tagged content;

extracting, with the processor, viral properties for the tagged content;

identifying, with the processor, a commentor from said tagged content and an individual cited in the tagged content contained within the web-site;

aggregating, with the processor, the viral properties for the tagged content from said web-site to form aggregated viral properties of the tagged content from said web-site, the viral properties of the tagged content from said commentor to form aggregated viral properties of the tagged content from said commentor, and the viral properties of the tagged content citing said individual to form aggregated viral properties of the tagged content citing said individual;

computing with the processor;

the topical influence value of said commentor based on a linear combination of the aggregated viral properties of the tagged content from said commentor weighted with respective weights applied to each of the aggregated viral properties of the tagged content from said commentor;

the influence value of said individual based on a linear combination of the aggregated viral properties of the tagged content citing said individual; and

the topical influence value of the web-site based on a linear combination of the aggregated viral properties of the tagged content from said web-site, the topical influence value of the commentor, and the influence value of said individual cited in the tagged content.

2. The method of claim 1, wherein the step of computing, with the processor:

the topical influence value of said commentor further comprises;

determining topical influences of on-line outlets owned by said commentor, said topical influences characterized by respective topical influence values of the on-line outlets; and

creating an updated commentor influence model by forming a linear combination of the topical influence values of said on-line outlets and the topical influence value of the commentor.

3. The method of claim 2, wherein each topical influence of the topical influences of the on-line outlets owned by said commentor is determined from a linear combination of viral properties extracted from a corresponding on-line outlet.

4. The method of claim 1, further comprising:

repeating the steps of matching and tagging, extracting, identifying, aggregating and computing, the repeating of the computing step including;

updating the topical influence value of said commentor to generate an updated topical influence value of said commentor;

updating the influence value of said individual to generate an updated influence value of said individual; and

updating the topical influence value of the web-site to generate an updated topical influence value of the web-site by integrating the updated topical influence value of the commentor and the updated influence value of said individual in the computation of the updated topical influence value of the web-site.

5. The method of claim 1, wherein the step of computing the influence value of said individual, further comprises:

identifying an outlet owned by said individual;

calculating an influence value of said outlet owned by said individual based on a linear combination of viral properties extracted from said outlet owned by said individual; and

updating the influence value of said individual based on a linear combination of said aggregated viral properties of the tagged content citing said individual and the influence value of said outlet owned by said individual.

6. The method as described in claim 1, wherein the step of extracting viral properties for the tagged content comprises:

collecting values of the viral properties at predetermined time intervals; and

storing the collected values in respective time series.

7. The method of claim 1 wherein said viral properties are selected from the group consisting of:

User engagement value;

average Comment count;

average unique commentor count;

cited individual count,

inbound links;

subscribers;

average Social bookmarks;

average Social news votes;

buries;

total count of posts; and

total count of appearance of Individuals names across all posts.

8. A method performed by a computer for determining a topical influence value of a commentor, an influence value of an individual, and a topical influence value of a web-site, wherein the computer is in communication with a server via network to access a web-site hosted by the server, the method comprising the steps of:

matching and tagging content within the web-site with a selected topic, using a processor of the computer, to form tagged content;

extracting, with the processor, viral properties for the tagged content;

identifying from said tagged content, with the processor, a commentor having a comment in said tagged content contained within the web-site and an individual cited in the tagged content contained within the web-site;

aggregating, with the processor, the viral properties for the tagged content from said web-site to form aggregated viral properties of the tagged content from said web-site, the viral properties across all tagged content contained within the web-site including viral properties of the tagged content from said commentor to form aggregated viral properties of the tagged content having the comment from said commentor, and the viral properties of the tagged content citing said individual to form aggregated viral properties of the tagged content citing said individual;

computing, with the processor;

the topical influence value of said commentor using a commentor influence model that is based on a linear combination of the aggregated viral properties of the tagged content having the comment from said commentor, the aggregated viral properties weighted with respective weights applied to each of the aggregated viral properties of the tagged content from said commentor;

the influence value of said individual based on a linear combination of the aggregated viral properties of the tagged content contained within the web-site and citing said individual; and

the topical influence value of the web-site based on a linear combination of the aggregated viral properties of the tagged content from said web-site, the topical influence value of the commentor and the influence value of said individual cited in the tagged content contained within the web-site, said topical influence value of the web-site characterizing the topical on-line influence of the web-site;

repeating the steps of matching and tagging, extracting, identifying, aggregating and computing, the repeating of the computing step including;

updating the topical influence value of said commentor using the commentor influence model to generate an updated topical influence value of said commentor;

updating the influence value of said individual to generate an updated influence value of said individual; and

updating the topical influence value of the web-site to generate an updated topical influence value of the web-site by integrating the updated topical influence value of the commentor and the updated influence value of said individual in the computation of the updated topical influence value of the web-site.

9. The method of claim 8, wherein the step of computing the topical influence value of said commentor comprises:

identifying an outlet owned by said commentor;

determining an influence of said outlet characterized by an influence value of said outlet; and

calculating the influence value of said commentor by including the influence value of said outlet in the linear combination of the aggregated viral properties of the tagged content contained within the web-site and having a comment from said commentor.

10. The method of claim 8, wherein the step of computing the influence value of said individual comprises:

identifying an outlet owned by said individual;

determining an influence of said outlet characterized by an influence value of said outlet; and

calculating the influence value of said individual by including the influence value of said outlet in the linear combination of the aggregated viral properties of the tagged content contained within the web-site and citing said individual.

11. The method of claim 8, further comprising:

iterating the steps of matching and tagging, extracting, identifying, aggregating and computing for a plurality of web-sites; and

identifying top influential sites based on the topical influence values for each of the plurality of web-sites.

12. The method of claim 8, further comprising:

iterating the steps of matching and tagging, extracting, identifying, aggregating and computing for a plurality of commenters; and

identifying top influential commentors based on the topical influence values for each of the plurality of commentors.

13. The method of claim 8, further comprising:

iterating the steps of matching and tagging, extracting, identifying, aggregating and computing for a plurality of individuals; and

identifying top influential individuals based on the influence values for each of the plurality of individuals.

14. A non-transitory computer readable medium, comprising computer code instructions stored thereon, which, when executed by a computer coupled to a network and in communication with a server via the network to access a web-site hosted by the server, cause the computer to perform the steps of:

matching and tagging content within a web-site with a selected topic to form tagged content;

extracting viral properties for the tagged content;

identifying a commentor from said tagged content and an individual cited in the tagged content contained within the web-site;

aggregating the viral properties for the tagged content from said web-site to form aggregated viral properties of the tagged content from said web-site, aggregating the viral properties of the tagged content having a comment from said commentor to form aggregated viral properties of the tagged content from said commentor, and aggregating the viral properties of the tagged content citing said individual to form aggregated viral properties of the tagged content citing said individual;

computing a topical influence value of said commentor based on a linear combination of the aggregated viral properties of the tagged content from said commentor weighted with respective weights applied to each of the aggregated viral properties;

computing an influence value of said individual based on a linear combination of the aggregated viral properties of the tagged content citing said individual; and

computing a topical influence value of the web-site based on a linear combination of the aggregated viral properties of the tagged content from said web-site, the topical influence value of the commentor and the influence value of said individual cited in the tagged content contained within the web-site.

15. A system for determining a topical influence of a web-site, comprising:

a network;

a server coupled to the network, wherein the server hosts the web-site;

a computer coupled to the network and in communication with the server to access the web-site, the computer comprising;

a processor; and

a computer readable storage medium having computer readable instructions stored thereon for execution by the processor, forming the following modules;

a content-to-topic matching module for matching and tagging content within the web-site to a selected topic to generate tagged content;

a viral properties extraction module for extracting viral properties of said tagged content, for identifying a commentor from said tagged content and an individual cited in the tagged content contained within the web-site, for aggregating the viral properties for the tagged content from said web-site to form aggregated viral properties of the tagged content from said web-site, for aggregating the viral properties of the tagged content having a comment from said commentor to form aggregated viral properties of the tagged content from said commentor, and for aggregating the viral properties of the tagged content citing said individual to form aggregated viral properties of the tagged content citing said individual;

a commentor influence modeling module for computing a topical influence value of the commentor based on a linear combination of the aggregated viral properties of the tagged content from said commentor weighted with respective weights applied to each of the aggregated viral properties;

an individual influence modeling module for computing, based on a linear combination of the aggregated viral properties extracted from the tagged content citing said individual, an influence value of said individual cited in said tagged content; and

a site influence modeling module for computing the topical influence value of the web-site based on a linear combination of the aggregated viral properties of the tagged content within the web-site, the topical influence value of the commentor, and the influence value of said individual cited in the tagged content contained; and

a database comprising;

a top influential individuals data store for storing the influence value;

a top influential commenters data store for storing the topical influence value of the commentor; and

a top influential sites data store for storing the topical influence value of the web-site.

16. The system of claim 15, wherein the commentor influence modeling module further comprises:

a commentor influence updating module for updating the influence of the commentor by calculating the topical influence value of the commentor based on viral properties extracted from the tagged content having a comment from the commentor and influence values of outlets owned by said commentor.

17. The system of claim 15, wherein the individual influence modeling module further comprises:

an individual influence updating module for updating the topical influence of the individual by calculating the influence value of the individual based on viral properties extracted from tagged content identifying said individual and influence values of outlets owned by said individual.