Determination of general and topical news and geographical scope of news content

• US 9,807,181 B2
• Filed: 07/17/2013
• Issued: 10/31/2017
• Est. Priority Date: 07/17/2013
• Status: Active Grant

• Current Assignee: Yahoo Assets LLC
• Original Assignee: Yahoo Holdings, Inc. (Verizon Communications Inc.)
• Inventors: Wexler, Mike
• Primary Examiner(s): Patel, Dhairya A
• Application Number:
  US13/944,409
• Publication Number:
  US 20150026255A1
• Time in Patent Office: 1,567 Days
• Field of Search: 709204, 709224, 709205, 707723, 707738
• CPC Class Codes: G06F 16/9537 Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
• CPC Class Codes: H04L 51/222 using geographical location information, e.g. messages transmitted or received in proximity of a certain spot or area
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04W 4/185 by embedding added-value information into content, e.g. geo-tagging

Abstract

Methods for categorizing news are presented. One method groups articles into clusters that share a common topic. A first category is identified for each article that indicates if the article is news or not. Further, the method includes an operation for determining use data for each article that has information about people that have accessed or referenced the article. Additionally, the method includes an operation for combining the use data and the first category for all the articles in each cluster to determine the geographical scope of interest for the cluster. The use data and the first category are combined for all the articles in each cluster to determine a second category for each article that indicates if the article is general news, topical news, or not news. The articles are presented to the user based on the geographical scope of interest, the second category, and the attributes of the user.

Claims

1. A method for categorizing news articles, the method comprising:

grouping articles into clusters, each cluster being associated with a topic that is common to articles in the cluster;

identifying a plurality of signals indicative of whether an article is considered news or not-news;

assigning a first category for each article that defines the article as news or not-news, wherein assigning the first category includes,

obtaining a score for each article based on the plurality of signals identified from each article, and

determining the first category as news when the score is above or equal to a predetermined threshold or not-news when the score is below the predetermined threshold;

obtaining use data for each article, the use data including social information gathered from one or more social networks of users that have accessed or referenced each article, wherein the social information includes geographical data associated with the users that have accessed or referenced each article within the one or more social networks, and wherein the use data further includes mapped locations of users that have accessed each article;

combining the use data and the first category for all the articles in each cluster to determine a geographical scope of interest for the cluster, wherein the geographical scope of interest for the cluster includes a geographic region in which one or more users are interested in one or more of the articles in the cluster, and wherein the geographical scope of interest for the cluster is further based on the mapped locations of users that have accessed the articles in the cluster;

combining the use data and the first category for all the articles in each cluster to determine a second category for each article, the second category indicating if the article is general news, topical news, or not-news; and

presenting the articles to a user based on the geographical scope of interest, the second category, and attributes of the user, wherein operations of the method are executed by a processor.

2. The method as recited in claim 1, wherein determining use data further includes:

identifying users of a social network that have referenced each article; and

determining a geographical location of the users that have accessed each article.

3. The method as recited in claim 1, wherein determining use data further includes:

identifying emails that refer to each article; and

determining a geographical location of users associated with the respective emails.

4. The method as recited in claim 1, wherein determining use data further includes:

identifying text messages that refer to each article; and

determining a geographical location of users associated with the respective text messages.

5. The method as recited in claim 1, wherein determining use data further includes:

identifying users that have performed web searches related to each article; and

determining a geographical location of users that have performed the web searches.

6. The method as recited in claim 1, wherein determining use data further includes:

identifying news publishers that have published each article; and

determining a geographical location of the news publishers.

7. The method as recited in claim 1, wherein determining use data further includes:

identifying users that have performed an operation indicating an approval for each article; and

determining a geographical location of the users.

8. The method as recited in claim 1, wherein identifying the first category further includes:

extracting topic information from each article based on an analysis of text in each article;

identifying categorizations given by article providers for each article; and

utilizing machine categorization based on machine learning of sample articles categorized by judges.

9. The method as recited in claim 8, wherein utilizing machine categorization further includes:

analyzing the sample articles to provide an automated classifier; and

utilizing the classifier with each of the articles.

10. The method as recited in claim 1, wherein determining the second category further includes:

identifying signals associated with each article; and

combining the signals to determine the second category for each article.

11. The method as recited in claim 1, wherein the attributes of the user includes one or more of user geographical location, user residence location, user birth location, news parameters configured by the user, user social activity, or user search history.

12. The method as recited in claim 1, wherein the plurality of signals are selected from a group consisting of social network activity of a user accessing the article, or geographic location of a user accessing the article, or geographic locations of users utilizing search engines to find the article, or geographic location of news publisher that published the article.

13. A computer program embedded in a non-transitory computer-readable storage medium, when executed by one or more processors, for categorizing news articles, the computer program comprising:

program instructions for grouping articles into clusters, each cluster being associated with a topic that is common to articles in the cluster;

program instructions for identifying a plurality of signals indicative of whether an article is considered news or not-news;

program instructions for assigning a first category for each article that defines the article as news or not-news, wherein assigning the first category includes,

obtaining a score for each article based on the plurality of signals identified from each article, and

determining the first category as news when the score is above or equal to a predetermined threshold or not-news when the score is below the predetermined threshold;

program instructions for obtaining use data for each article, the use data including social information gathered from one or more social networks of users that have accessed or referenced each article, wherein the social information includes geographical data associated with the users that have accessed or referenced each article within the one or more social networks, and wherein the use data further includes mapped locations of users that have accessed each article;

program instructions for combining the use data and the first category for all the articles in each cluster to determine a geographical scope of interest for the cluster, wherein the geographical scope of interest for the cluster includes a geographic region in which one or more users are interested in one or more of the articles in the cluster, and wherein the geographical scope of interest for the cluster is further based on the mapped locations of users that have accessed the articles in the cluster;

program instructions for combining the use data and the first category for all the articles in each cluster to determine a second category for each article, the second category indicating if the article is general news, topical news, or not-news; and

program instructions for presenting the articles to a user based on the geographical scope of interest, the second category, and attributes of the user.

14. The computer program as recited in claim 13, wherein determining use data further includes:

identifying instant messages that refer to each article; and

determining a geographical location of users associated with the respective instant messages.

15. The computer program as recited in claim 13, wherein determining use data further includes:

identifying emails that refer to each article; and

determining a geographical location of users associated with the respective emails.

16. The computer program as recited in claim 13, wherein determining use data further includes:

identifying users that have performed web searches related to each article; and

determining a geographical location of users that have performed the web searches.

17. The computer program as recited in claim 13, wherein determining use data further includes:

identifying news publishers that have published each article; and

determining a geographical location of the news publishers.

18. The computer program as recited in claim 13, wherein determining use data further includes:

identifying users that have performed an operation indicating an approval for each article; and

determining a geographical location of the users.

19. A system for categorizing news articles, the system comprising:

a processor; and

a memory having a computer program, wherein program instructions from the computer program when executed by the processor cause the processor to;

group articles into clusters, each cluster being associated with a topic that is common to articles in the cluster;

identify a plurality of signals indicative of whether an article is considered news or not-news;

assign a first category for each article, the first category defining the article as news or not-news, wherein assigning the first category includes,

obtaining a score for each article based on the plurality of signals identified from each article, and

determining the first category as news when the score is above or equal to a predetermined threshold or not-news when the score is below the predetermined threshold;

obtain use data for each article, the use data including social information gathered from one or more social networks of users that have accessed or referenced each article, wherein the social information includes geographical data associated with the users that have accessed or referenced each article within the one or more social networks, and wherein the use data further includes mapped locations of users that have accessed each article;

combine the use data and the first category for all the articles in each cluster to determine a geographical scope of interest for the cluster, wherein the geographical scope of interest for the cluster includes a geographic region in which one or more users are interested in one or more of the articles in the cluster, and wherein the geographical scope of interest for the cluster is further based on the mapped locations of users that have accessed the articles in the cluster;

combine the use data and the first category for all the articles in each cluster to determine a second category for each article, the second category indicating if the article is general news, topical news, or not-news; and

present the articles to a user based on the geographical scope of interest, the second category, and attributes of the user.

20. The system of claim 19, wherein determining the use data further includes:

identifying one of an endorsement, a like, a thumbs-up, a recommendation, or a tweet of the users that have accessed or referenced each article; and

determining a geographical location of the users that have accessed or referenced each article.