Methods and apparatus to identify non-traditional asset-bundles for purchasing groups using social media
Methods, apparatus, systems and articles of manufacture are disclosed to identify non-traditional asset-bundles for purchasing groups using social media. An example method includes identifying an asset-bundle in a social media message and generating a profile for a user associated with the social media message. The example method also includes identifying a plurality of social media messages posted by cohorts of the user based on the generated profile and classifying the asset-bundle based on occurrences of the asset-bundle in the plurality of social media messages.
1. An apparatus comprising:
means for identifying an asset to:
parse a first network communication received from a social media server, to identify a social media message that references a first asset;
analyze the social media message to identify a second asset in the social media message;
associate the first asset and the second asset in an asset-bundle;
determine if the asset-bundle has been previously identified to reduce an amount of processing requirements of a central facility; and
means for generating a profile to:
generate a profile for a user associated with the social media message;
determine if the profile for the user is already stored in memory of a central facility;
in response to determining that the profile for the user is already stored in memory of the central facility, foregoing storage of the generated profile of the user to reduce a storage requirement of the central facility; and
means for classifying a bundle to classify the asset-bundle based on occurrences of the asset-bundle in a plurality of social media messages.
2. The apparatus as defined in claim 1 , wherein the means for generating a profile is to:
analyze a second plurality of social media messages for characteristic elements, each of the second plurality of social media messages associated with the user; and
apply Bayesian analysis to the characteristic elements to generate the profile for the user.
3. The apparatus as defined in claim 1 , wherein the means for classifying the bundle is to classify the asset-bundle by:
determining a count for the asset-bundle based on a number of occurrences of the asset bundle in the plurality of social media messages;
comparing the count for the asset-bundle to a first threshold; and
classifying the asset-bundle a coincidental pairing when the count for the asset-bundle does not satisfy the first threshold.
4. The apparatus as defined in claim 3 , wherein the means for classifying the bundle is to:
compare the count to a second threshold when the count satisfies the first threshold; and
classify the asset-bundle a traditional pairing based on a determination that the count for the asset-bundle satisfies the second threshold.
5. The apparatus as defined in claim 4 , wherein the means for classifying the bundle is to classify the asset-bundle as a non-traditional pairing based on a determination that the count for the asset-bundle does not satisfy the second threshold.
6. The apparatus as defined in claim 3 , wherein the means for classifying the bundle is to:
identify a first category for a first asset of the asset-bundle when the count for the asset-bundle satisfies the first threshold;
identify a second category for a second asset of the asset-bundle; and
classify the asset-bundle as a traditional pairing based on a determination that the first category is related to the second category.
7. A non-transitory tangible machine readable storage medium comprising instructions that, when executed, cause a processor to at least:
parse a first network communication received from a social media server, to identify a social media message that references a first asset;
analyze the social media message to identify a second asset in the social media message;
associate the first asset and the second asset in an asset-bundle;
determine if the asset-bundle has been previously identified to reduce an amount of processing requirements of a central facility;
generate a profile for a user associated with the social media message;
determine if the profile for the user is already stored in memory of a central facility;
in response to determining that the profile for the user is already stored in memory of the central facility, foregoing storage of the generated profile of the user to reduce a storage requirement of the central facility; and
classify the asset-bundle based on occurrences of the asset-bundle in a plurality of social media messages.
8. The non-transitory tangible machine readable storage medium as defined in claim 7 , wherein the instructions are further to cause the processor to generate the profile for the user by:
analyzing a second plurality of social media messages for characteristic elements, each of the second plurality of social media messages associated with the user; and
applying statistical methods to the characteristic elements to generate the profile for the user.
9. The non-transitory tangible machine readable storage medium as defined in claim 7 , wherein the instructions are further to cause the processor to classify the asset-bundle by:
determining a count for the asset-bundle based on a number of occurrences of the asset bundle in the plurality of social media messages;
comparing the count for the asset-bundle to a first threshold; and
classifying the asset-bundle a coincidental pairing based on a determination that the count for the asset-bundle does not satisfy the first threshold.
10. The non-transitory tangible machine readable storage medium as defined in claim 9 , wherein the instructions are further to cause the processor to:
compare the count to a second threshold when the count satisfies the first threshold; and
classify the asset-bundle a traditional pairing based on a determination that the count for the asset-bundle satisfies the second threshold.
11. The non-transitory tangible machine readable storage medium as defined in claim 10 , wherein the instructions are further to cause the processor to classify the asset-bundle as a non-traditional pairing based on a determination that the count for the asset-bundle does not satisfy the second threshold.
12. The non-transitory tangible machine readable storage medium as defined in claim 9 , wherein the instructions are further to cause the processor to:
identify a first category for a first asset of the asset-bundle when the count for the asset-bundle satisfies the first threshold;
identify a second category for a second asset of the asset-bundle; and
classify the asset-bundle as a traditional pairing based on a determination that the first category is related to the second category.
13. The non-transitory tangible machine readable storage medium as defined in claim 12 , wherein the instructions are further to cause the processor to classify the asset-bundle as a non-traditional pairing based on a determination that the first category is not related to the second category.
14. A method comprising:
parsing, by executing an instruction with a processor at an audience measurement entity server, a first network communication received from a social media server to identify a social media message that references a first asset;
analyzing, by executing an instruction with the processor, the social media message using image recognition to identify a second asset in the social media message;
associating, by executing an instruction with the processor, the first asset and the second asset in an asset-bundle;
determining, by executing an instruction with the processor, if the asset-bundle has been previously identified to reduce an amount of processing requirements of a central facility;
generating, by executing an instruction with the processor, a profile for a user associated with the social media message;
determining, by executing an instruction with the processor, if the profile for the user is already stored in memory of a central facility;
in response to determining that the profile for the user is already stored in memory of the central facility, foregoing storage of the generated profile of the user to reduce a storage requirement of the central facility; and
classifying, by executing an instruction with the processor, the asset-bundle based on occurrences of the asset-bundle in a plurality of social media messages.
15. The method as defined in claim 14 , wherein generating the profile for the user comprises:
analyzing a second plurality of social media messages for characteristic elements, each of the second plurality of social media messages associated with the user; and
applying statistical methods to the characteristic elements to generate the profile for the user.
16. The method as defined in claim 14 , further including sending a second network communication from the audience measurement entity server to the social media server, the second network communication requesting social media messages.
17. The method as defined in claim 14 , further including identifying the plurality of social media messages by:
identifying profiles that share a characteristic element included in the profile; and
requesting social media messages posted by users associated with the identified profiles.
18. The method as defined in claim 14 , wherein classifying the asset-bundle comprises:
determining a count for the asset-bundle based on a number of occurrences of the asset bundle in the plurality of social media messages;
comparing the count for the asset-bundle to a first threshold; and
classifying the asset-bundle a coincidental pairing when the count for the asset-bundle does not satisfy the first threshold.
19. The method as defined in claim 18 further comprising:
comparing the count to a second threshold when the count satisfies the first threshold; and
classifying the asset-bundle a traditional pairing when the count for the asset-bundle satisfies the second threshold.
20. The method as defined in claim 19 , further comprising classifying the asset-bundle as a non-traditional pairing when the count for the asset-bundle does not satisfy the second threshold.
21. An apparatus comprising:
at least one memory;
instructions stored in the apparatus; and
processor circuitry to execute the instructions to:
parse a first network communication received from a social media server, to identify a social media message that references a first asset;
analyze the social media message to identify a second asset in the social media message,
associate the first asset and the second asset in an asset-bundle;
determine if the asset-bundle has been previously identified to reduce an amount of processing requirements of a central facility,
generate a profile for a user associated with the social media message,
determine if the profile for the user is already stored in memory of a central facility,
in response to determining that the profile for the user is already stored in memory of the central facility, foregoing storage of the generated profile of the user to reduce a storage requirement of the central facility, and
classify the asset-bundle based on occurrences of the asset-bundle in a plurality of social media messages.
22. The apparatus as defined in claim 21 , wherein the processor circuitry is to execute the instructions to generate the profile for the user by:
analyzing a second plurality of social media messages for characteristic elements, each of the second plurality of social media messages associated with the user; and
applying statistical methods to the characteristic elements to generate the profile for the user.
23. The apparatus as defined in claim 21 , wherein the processor circuitry is to execute the instructions to classify the asset-bundle by:
determining a count for the asset-bundle based on a number of occurrences of the asset bundle in the plurality of social media messages;
comparing the count for the asset-bundle to a first threshold; and
classifying the asset-bundle a coincidental pairing based on a determination that the count for the asset-bundle does not satisfy the first threshold.
24. The apparatus as defined in claim 23 , wherein the processor circuitry is to execute the instructions to further:
compare the count to a second threshold when the count satisfies the first threshold; and
classify the asset-bundle a traditional pairing based on a determination that the count for the asset-bundle satisfies the second threshold.
25. The apparatus as defined in claim 24 , wherein the processor circuitry is to execute the instructions to further classify the asset-bundle as a non-traditional pairing based on a determination that the count for the asset-bundle does not satisfy the second threshold.
26. The apparatus as defined in claim 23 , wherein the processor circuitry is to execute the instructions to further:
identify a first category for a first asset of the asset-bundle when the count for the asset-bundle satisfies the first threshold;
identify a second category for a second asset of the asset-bundle; and
classify the asset-bundle as a traditional pairing based on a determination that the first category is related to the second category.
27. The apparatus as defined in claim 26 , wherein the processor circuitry is to execute the instructions to further to classify the asset-bundle as a non-traditional pairing based on a determination that the first category is not related to the second category.