Methods and apparatus to identify non-traditional asset-bundles for purchasing groups using social media

Abstract

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

What is claimed is: 
     
         1 . An apparatus comprising:
 at least one memory;   instructions stored in the apparatus; and   processor circuitry to execute the instructions to:
 parse a network communication received from a social media server to identify a social media message that references a first asset, 
 analyze the social media message using image recognition 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, 
 store the asset-bundle in memory of a central facility in response to determining that the asset-bundle had not been previously identified, 
 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 the determination that the profile for the user is already stored in memory of the central facility, forego storage of the generated profile of the user to reduce an amount of storage requirements of the central facility, 
 identify a plurality of social media messages posted by cohorts of the user based on the generated profile, and 
 classify the asset-bundle based on occurrences of the asset-bundle in the plurality of social media messages, the classification to be stored in a memory of an audience measurement entity server. 
   
     
     
         2 . The apparatus as defined in  claim 1 , wherein, to generate the profile for the user, the processor circuitry 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 statistical methods to the characteristic elements to generate the profile for the user.   
     
     
         3 . The apparatus as defined in  claim 2 , wherein the statistical methods applied by the processor circuitry includes a Bayesian analysis. 
     
     
         4 . The apparatus as defined in  claim 1 , wherein to identify the plurality of social media messages posted by cohorts of the user, the processor circuitry is to:
 identify profiles that share a characteristic element included in the profile; and   request social media messages posted by users associated with the identified profiles.   
     
     
         5 . The apparatus as defined in  claim 1 , wherein, to classify the asset-bundle, the processor circuitry is to:
 determine a count for the asset-bundle based on a number of occurrences of the asset bundle in the plurality of social media messages;   compare the count for the asset-bundle to a first threshold; and   classify the asset-bundle a coincidental pairing when the count for the asset-bundle does not satisfy the first threshold.   
     
     
         6 . The apparatus as defined in  claim 5 , wherein the processor circuitry is to:
 compare the count to a second threshold when the count satisfies the first threshold; and   classify the asset-bundle as a traditional pairing when the count for the asset-bundle satisfies the second threshold.   
     
     
         7 . The apparatus as defined in  claim 6 , wherein the processor circuitry is to classify the asset-bundle as a non-traditional pairing when the count for the asset-bundle does not satisfy the second threshold. 
     
     
         8 . The apparatus as defined in  claim 7 , wherein the processor circuitry is to:
 when the count for the asset-bundle satisfies the first threshold, identify a first category for a first asset of the asset-bundle;   identify a second category for a second asset of the asset-bundle; and   classify the asset-bundle as a traditional pairing when the first category is related to the second category.   
     
     
         9 . The apparatus as defined in  claim 8 , wherein the processor circuitry is to classify the asset-bundle as a non-traditional pairing when the first category is not related to the second category. 
     
     
         10 . An apparatus comprising:
 at least one memory;   instructions stored in the apparatus; and   processor circuitry to execute the instructions to:
 identify constraints of a purchasing group, 
 identify user profiles that satisfy constraints of the purchasing group, 
 identify social media messages of interest posted by users associated with the identified user profiles, 
 determine an asset-bundle count for an asset-bundle referenced in the social media messages of interest, 
 determine if the asset-bundle count satisfies a threshold, and 
 classify the asset-bundle based on the determination of whether the asset bundle satisfies the threshold. 
   
     
     
         11 . The apparatus as defined in  claim 10 , wherein the processor circuitry is to compare the asset bundle count to a coincidence threshold to classify the asset bundle as coincidental pairing or a non-coincidental pairing. 
     
     
         12 . The apparatus as defined in  claim 10 , wherein the processor circuitry is to compare the asset bundle count to a traditional threshold to classify the asset bundle as traditional or non-traditional. 
     
     
         13 . The apparatus as defined in  claim 10 , wherein the processor circuitry is to determine if the asset-bundle count satisfies the threshold based on counting a number of times the asset-bundle is referenced in the social media messages of interest. 
     
     
         14 . The apparatus as defined in  claim 10 , wherein the processor circuitry is to identify the social media messages of interest based on message identifiers associated with the identified user profiles. 
     
     
         15 . A non-transitory computer readable medium comprising instructions, which when executed, cause at least one processor to:
 identify constraints of a purchasing group;   identify user profiles that satisfy the constraints of the purchasing group;   identify social media messages of interest posted by users associated with the identified user profiles;   determine an asset-bundle count for an asset-bundle referenced in the social media messages of interest;   determine if the asset-bundle count satisfies a threshold; and   classify the asset-bundle based on the determination of whether the asset bundle satisfies the threshold.   
     
     
         16 . The non-transitory computer readable medium as defined in  claim 15 , wherein the asset-bundle is classified as a coincidental pairing or a non-coincidental pairing based on comparing the asset bundle count to a coincidence threshold. 
     
     
         17 . The non-transitory computer readable medium as defined in  claim 16 , wherein the asset-bundle is classified as traditional or non-traditional based on comparing the asset bundle count to a traditional threshold. 
     
     
         18 . The non-transitory computer readable medium as defined in  claim 15 , wherein the asset-bundle is classified as traditional or non-traditional based on comparing the asset bundle count to a traditional threshold. 
     
     
         19 . The non-transitory computer readable medium as defined in  claim 15 , wherein the determination of whether the asset-bundle count satisfies the threshold is based on counting a number of times the asset-bundle is referenced in the social media messages of interest. 
     
     
         20 . The non-transitory computer readable medium as defined in  claim 15 , wherein the social media messages of interest are identified based on message identifiers associated with the identified user profiles. 
     
     
         21 . A method comprising:
 identifying, by executing instructions with at least one processor, constraints of a purchasing group;   identifying, by executing instructions with the at least one processor, user profiles that satisfy the constraints of the purchasing group;   identifying, by executing instructions with the at least one processor, social media messages of interest posted by users associated with the identified user profiles;   determining, by executing instructions with the at least one processor, an asset-bundle count for an asset-bundle referenced in the social media messages of interest;   determining, by executing instructions with the at least one processor, if the asset-bundle count satisfies a threshold; and   classifying, by executing instructions with the at least one processor, the asset-bundle based on the determination of whether the asset bundle satisfies the threshold.   
     
     
         22 . The method as defined in  claim 21 , wherein the determining if the asset-bundle count satisfies the threshold includes comparing the asset bundle count to a coincidence threshold to classify the asset bundle as coincidental pairing or a non-coincidental pairing. 
     
     
         23 . The method as defined in  claim 21 , wherein the determining if the asset-bundle count satisfies the threshold includes comparing the asset bundle count to a traditional threshold to classify the asset bundle as traditional or non-traditional. 
     
     
         24 . The method as defined in  claim 21 , wherein the determining of whether the asset-bundle count satisfies the threshold is based on counting a number of times the asset-bundle is referenced in the social media messages of interest. 
     
     
         25 . The method as defined in  claim 21 , wherein the identifying of the social media messages of interest is based on message identifiers associated with the identified user profiles.