IDENTIFYING SOURCES OF MEDIA CONTENT HAVING A HIGH LIKELIHOOD OF PRODUCING ON-TOPIC CONTENT

• US 20080114755A1
• Filed: 11/12/2007
• Published: 05/15/2008
• Est. Priority Date: 11/15/2006
• Status: Abandoned Application

• Current Assignee: Collective Intellect, Inc. (Oracle Corporation)
• Original Assignee: Collective Intellect, Inc. (Oracle Corporation)
• Inventors: Wolters, Timothy J., Setayesh, Mehrshad
• Application Number:
  US11/938,691
• Publication Number:
  US 20080114755A1
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06F 16/954 Navigation, e.g. using categorised browsing

Abstract

Methods and systems are provided for identifying on-topic sources of media content. According to one embodiment, candidate seed sites are identified from which current seeds are selected for deep crawling. The current seeds are identified by correlating relevancy scores or key-word search results from multiple search engines; and selecting the current seeds based on on-topic scores of the candidate seeds. Periodically, a topic net associated with the topic area of interest is executed to locate relevant sources of media content by (i) building a graph in which nodes represent pages and edges represent links among pages by performing an iterative 360 crawl starting from the seeds; (ii) assigning initial node graph scores; (iii) computing final node graph scores by performing link analysis; (iv) computing a site graph scores by aggregating and averaging corresponding node graph scores; and (v) configuring sites with the highest site graph scores to be scraped.

Claims

1. A method of identifying sources of media content having a high likelihood of producing on-topic content, the method comprising:

responsive to receiving a definition of a topic area of interest of a plurality of topic areas of interest, identifying a set of candidate seed sites from which a current set of seeds are selected for deep crawling to locate on-topic content relevant to the topic area of interest by

correlating relevancy scores or key-word search results from a plurality of search engines; and

selecting the current set of seeds from the candidate seed sites based at least in part on on-topic scores associated with the candidate seed sites;

periodically executing a topic net corresponding to the topic area of interest to locate sources of media content relevant to the topic area of interest by

building a graph in which nodes of the graph represent pages and edges of the graph represent links among pages by performing an iterative crawl until a predetermined degree of separation is achieved to find a list of pages linking to any seed of the current set of seeds and a list of pages to which any seed of the current set of seeds links;

assigning initial graph scores to each node of the graph;

computing final graph scores for each node based on the initial graph scores by performing link analysis on the graph;

computing a site graph score for each site represented in the graph by its set of pages by aggregating and averaging the node graph scores associated with the site; and

identifying a set of sites with the highest site graph scores and configuring them to be scraped; and

scraping and downloading pages associated with the sites configured to be scraped.

2. The method of claim 1, wherein the set of sites and the current set of seeds comprise weblogs (blogs) and the pages comprise blog posts.

3. The method of claim 2, further comprising measuring the health of the plurality of topic areas of interest by performing health analysis.

4. The method of claim 3, wherein said performing health analysis comprises:

producing metrics relating to various health parameters for each site of the set of sites and each seed of the current set of seeds, including a number of new posts created and an average post relevancy score, by evaluating posts associated with the set of sites and the current set of seeds; and

adding or subtracting seeds from the current set of seeds for use in a next topic net execution iteration based on the metrics.

5. The method of claim 2, further comprising creating a quality centrality measure for each of the plurality of topic areas of interest.

6. The method of claim 5, wherein the quality centrality measure is based upon latent semantic analysis.

7. The method of claim 2, wherein the initial graph scores are based upon one or more of a topic density score, a maven density score and a relevancy score.

8. The method of claim 2, further comprising prior to selecting the current set of seeds from the candidate seed sites, performing filtering to remove spam blogs from the candidate seed sites.

9. The method of claim 8, wherein the filtering comprises use of a spam blog (splog) detector comprising a text classification engine that discriminates between uniform resource locators (URLs) of legitimate blog home pages and splog home pages.

10. A computer program product, for use with a computer system, for directing the computer system to identify sources of media content having a high likelihood of producing on-topic content, the computer program product comprising:

a computer-readable medium;

means, provided on the computer-readable medium, for directing the computer system to identifying a set of candidate seed sites from which a current set of seeds are selected for deep crawling to locate on-topic content relevant to a topic area of interest of a plurality of topic areas of interest by

correlating relevancy scores or key-word search results from a plurality of search engines; and

selecting the current set of seeds from the candidate seed sites based at least in part on on-topic scores associated with the candidate seed sites;

means, provided on the computer-readable medium, for directing the computer system to periodically executing a topic net corresponding to the topic area of interest to locate sources of media content relevant to the topic area of interest by

building a graph in which nodes of the graph represent pages and edges of the graph represent links among pages by performing an iterative crawl until a predetermined degree of separation is achieved to find a list of pages linking to any seed of the current set of seeds and a list of pages to which any seed of the current set of seeds links;

assigning initial graph scores to each node of the graph;

computing final graph scores for each node based on the initial graph scores by performing link analysis on the graph;

computing a site graph score for each site represented in the graph by its set of pages by aggregating and averaging the node graph scores associated with the site; and

identifying a set of sites with the highest site graph scores and configuring them to be scraped; and

scraping and downloading pages associated with the sites configured to be scraped.

11. The computer program product of claim 10, wherein the set of sites and the current set of seeds comprise weblogs (blogs) and the pages comprise blog posts.

12. The computer program product of claim 11, further comprising means, provided on the computer-readable medium, for directing the computer system to measure the health of the plurality of topic areas of interest by performing health analysis.

13. The computer program product of claim 12, wherein said performing health analysis comprises:

producing metrics relating to various health parameters for each site of the set of sites and each seed of the current set of seeds, including a number of new posts created and an average post relevancy score, by evaluating posts associated with the set of sites and the current set of seeds; and

adding or subtracting seeds from the current set of seeds for use in a next topic net execution iteration based on the metrics.

14. The computer program product of claim 11, further comprising means, provided on the computer-readable medium, for directing the computer system to create a quality centrality measure for each of the plurality of topic areas of interest.

15. The computer program product of claim 14, wherein the quality centrality measure is based upon latent semantic analysis.

16. The computer program product of claim 11, wherein the initial graph scores are based upon one or more of a topic density score, a maven density score and a relevancy score.

17. The computer program product of claim 11, further comprising means, provided on the computer-readable medium, for directing the computer system to prior to selection of the current set of seeds from the candidate seed sites, perform filtering to remove spam blogs from the candidate seed sites.

18. The computer program product of claim 17, wherein the filtering comprises use of a spam blog (splog) detector comprising a text classification engine that discriminates between uniform resource locators (URLs) of legitimate blog home pages and splog home pages.