Location estimation of social network users

• US 8,990,327 B2
• Filed: 06/04/2012
• Issued: 03/24/2015
• Est. Priority Date: 06/04/2012
• Status: Active Grant

• Current Assignee: Snap, Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: Drews, Clemens, Mahmud, Jalal U., Nichols, Jeffrey W.
• Primary Examiner(s): Avellino, Joseph E
• Assistant Examiner(s): Kunwar, Binod J
• Application Number:
  US13/487,855
• Publication Number:
  US 20130325975A1
• Time in Patent Office: 1,023 Days
• Field of Search: 709/206
• US Class Current: 709/206
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 51/52 for supporting social networking services

Abstract

Various embodiments of the invention relate to estimating the location of social network users. In one embodiment, a plurality of social media messages generated by a given user is received. A plurality of location features is extracted from the social media messages. Each of the location features is processed with at least one classifier from an ensemble of classifiers. A location classification is generated by each of the classifiers for each of the social media messages. Each classification comprises a location and a weight associated with that location. One of the locations is selected from the location classifications as the location of the given user based on a combination of the weights of the location classifications.

Claims

1. A system comprising:

a memory;

a processor communicatively coupled to the memory;

a location estimator communicatively coupled to the memory and the processor, wherein the test location estimator is configured to;

receive a plurality of social media messages generated by a given user;

extract a plurality of location features from the social media messages;

compute, for each of the plurality of location features, a frequency of the location feature for at least one location;

determine, for each of the plurality of location features, a number of people in the at least one location who have used the location feature in their social networking messages;

determine, for each of the plurality of location features and based on the computed frequency and the determined number of people, if the location feature was included within social networking messages of a threshold percentage of people in the at least on location;

based on the location feature having been included within social networking messages of the threshold percentage of people;

adding the feature to the subset of features;

identify at least a subset of location features from the plurality of location features that are discriminative of at least one location at a location granularity level of interest;

process each of the subset of location features with at least one classifier from an ensemble of classifiers;

generate, by each of the classifiers, a location classification for each of the social media messages, each location classification comprising a location and a weight associated with that location; and

select one of the locations from the location classifications as the location of the given user based on a combination of the weights of the location classifications.

2. The system of claim 1,

wherein the ensemble of classifiers is a hierarchical ensemble of classifiers, and each classifier in a given level of the hierarchical ensemble of classifiers identifies a location at a smaller granularity than each classifier in a higher level of the hierarchical ensemble of classifiers.

3. The system of claim 2, wherein the location estimator is configure to generate a location classification by:

identifying, by each classifier at a first level of the hierarchical ensemble of classifiers, a time-zone location for the given user; and

identifying, by each classifier at a second level of the hierarchical ensemble of classifiers that is below the first level, a city location for the given user based partially on the time-zone location identified in the first level.

4. The system of claim 2, wherein the location estimator is configure to generate a location classification by:

identifying, by each classifier at a first level of the hierarchical ensemble of classifiers, a state location for the given user; and

identifying, by each classifier at a second level of the hierarchical ensemble of classifiers that is below the first level, a city location for the given user based partially on the state location identified in the first level.

5. The system of claim 2, wherein the location estimator is configure to generate a location classification by:

identifying, by each classifier at a first level of the hierarchical ensemble of classifiers, a geographical region location associated with the given user; and

identifying, by each classifier at a second level of the hierarchical ensemble of classifiers that is below the first level, a city location for the given user based partially on the geographical region location identified in the first level.

6. The system of claim 1, wherein the location estimator is configure to process by:

determining, by a binary classifier associated with one of the classifiers in the ensemble of classifiers, if a location associated with a given user is predictable by the one classifier; and

preventing the one classifier from generating the location classification if the binary classifier determines that the location is not predictable by the one classifier.

7. A computer program product comprising:

a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code configured to;

receive a plurality of social media messages generated by a given user;

extract a plurality of location features from the social media messages;

process each of the location features with at least one classifier from an ensemble of classifiers, wherein processing each of the location features comprises determining, by a binary classifier associated with one of the classifiers in the ensemble of classifiers, if a location associated with a given user is predictable by the on classifier; and

preventing the one classifier from generating the location classification if the binary classifier determines that the location is not predictable by the one classifier;

generate, by each of the classifiers, a location classification for each of the social media messages, each location classification comprising a location and a weight associated with that location; and

select one of the locations from the location classifications as the location of the given user based on a combination of the weights of the location classifications.

8. The computer program product of claim 7,

wherein the ensemble of classifiers is a hierarchical ensemble of classifiers, and each classifier in a given level of the hierarchical ensemble of classifiers identifies a location at a smaller granularity than each classifier in a higher level of the hierarchical ensemble of classifiers.

9. The computer program product of claim 8, wherein the computer readable program code is configured to generate a location classification by:

identifying, by each classifier at a first level of the hierarchical ensemble of classifiers, a time-zone location for the given user; and

identifying, by each classifier at a second level of the hierarchical ensemble of classifiers that is below the first level, a city location for the given user based partially on the time-zone location identified in the first level.

10. The computer program product of claim 8, wherein the computer readable program code is configured to generate a location classification by:

identifying, by each classifier at a first level of the hierarchical ensemble of classifiers, a state location for the given user; and

identifying, by each classifier at a second level of the hierarchical ensemble of classifiers that is below the first level, a city location for the given user based partially on the state location identified in the first level.

11. The computer program product of claim 8, wherein the computer readable program code is configured to generate a location classification by:

identifying, by each classifier at a first level of the hierarchical ensemble of classifiers, a geographical region location associated with the given user; and

identifying, by each classifier at a second level of the hierarchical ensemble of classifiers that is below the first level, a city location for the given user based partially on the geographical region location identified in the first level.

12. The computer program product of claim 7, wherein the computer readable program code is configured to processing by:

processing a subset of the location features with at least one behavior-based classifier, the subset of location features comprising a frequency of social media messages generated by the given user for a plurality of time durations.