Daily auto post 01/19/2012

  • An older article 2008 but shows how some people caught onto the social connections buried within search patents. Thanks David Harry for another informative read!! 

    Tags: socialgraph, socialdata, target, advanced, advertising, marketing, davidharry, seodojo, search, searchengines

    • In addition to that, data can be taken from content submitted by the user as well as content from friends and groups they are involved in. Taken in part or in the aggregate, the relationships and topical data provide insight into potential advertising targeting.

      There are also provisions for providing less weight or discounting altogether profile data, content submissions that may not meet thresholds of the content type. Once again, phrase relationships can be used in the analysis.

      Actually, phrase analysis was also mentioned as a tool for finding entity topics. By identifying the most frequently occurring keywords/phrases or even grammatical elements (nouns, verbs) more topical data can be obtained for tagging the user profile.

      And how about some data not related to the network? We have that too;

      ”For example, entity relationships and entities can be identified by processing web logs, e.g., blogs, AND processing web-based communities, e.g., homeowners associations, fan sites, etc., by processing company intranets, and by processing other data sources.”

      In short, there are no limits to the types of data that can be mined (not scraping are they?) to find user relationships and associated profile and content signals. By then monitoring the performance of the delivered content (ads or other content) the system could then tweak what it displays as it learns from its hits and misses.

      Interestingly, this data can be used not only for targeting advertising, but ultimately for suggesting content, groups and even other users you may be interested in.

      • ustodian Profiles

        The system when targeting a given content type could look at the user profile associated with the content in questions eg. When serving an ad on a given page within the network to another user. These are called, at least in this filing, custodian profiles.

        OpenSocialAccount information that can be used include;

        1. user profile data
        2. user acquaintance data
        3. user group data
        4. user media data
        5. user options data
        6. and other user data

        The user profile can, for example, also include general demographic data, such as age, sex, location, interests, etc. Or it could include professional information, e.g., occupation, educational background, etc., and other data, such as contact information. Depending on the social network, the types of data that can be used varies.

    • The elements can then be considered as a ‘custodian profile’. As an example the ‘user media data’ or content submitted to a social network for example, can be assigned to the user custodian account. The system can use information within custodian profiles to targeting signals as such;

      “… the custodian profile data may include professional information such as “Fishing Guide,” geographic information, such as “Key West, Fla.,” and a list of interests related to fishing and boating.

      The keywords can be provided to the content serving system, which can, for example, serve advertisements relating to Key West fishing guides.”

      The custodian of the content’s profile can be used as can that of the viewer (if known). This can lead to a hybrid delivery based on both the viewer profile and the custodian profile;

      “….. the viewer profile data may include hobby information such as “deep sea fishing,” geographic information, such as “Seattle, Wash.,” and a list of interests related to deep sea fishing.

      The keywords can be provided to the content serving system, which can, for example, serve advertisements relating to Key West deep sea fishing guides and travel options between Seattle and Key West. “

    • Knowing more about the profile of a user means the system can not only target content and ads based on related profile data, but assign performance metrics as well. For example the viewing of a given web page in the network could constitute and interest in that topic. Furthermore content/ad performance can be tracked over time intervals (such as weekends, time of day etc..). Data collected from the profile, associations and behavioural aspects can better target future content delivery.

      Taking into account the data from the person related with the content (ie; news submission, discussion etc..) potential relevance can be gained in serving advertisements and so on. Essentially, if you are viewing a page there is every potential that you may also be interested in topics related to the user that created the content. This is especially handy for ad serving to a visitor that may not be part of the social network or not logged in.

    • You’re not paranoid, you really are being watched

      Ultimately each of the methods adds a layer to the user targeting system. If a given web page on a social network has incomplete or a lack of content, on page semantic targeting won’t really produce content suggestions/advertisements that are relevant to the user. By looking at open profile data, associations and custodian signals, elements beyond mere semantic matching, greater targeting can be achieved.

      • Given all of this here’s a potential framework of elements for targeting social network users;

        Open Profile (user identification) – Uses identification and scoring/categorization analysis of a user profiles

        1. demographic data
        2. interest/topic categorization
        3. media data
        4. group associations
        5. influencer score
      • Custodian profile (content relational) – Looks at inferences between the viewer of a web page and the person that created, or manages it.

        1. user profile data
        2. user acquaintance data
        3. user group data
        4. user media data
        5. user options data
        6. and other user data
      • Relationships and Topics – For looking at common relationships among users and related topical categorizations (and performance metrics).

        1. Common groups
        2. Common behaviour
        3. Similar membership in groups
        4. Similar profile data
        5. Common acquaintances
        6. Other similarities
    • hile the whole ‘friend rank’ approach can find the influencers, the systems mentioned in these patents seem far more flexible in identifying broader social targeting. Sure, there are those that might have concerns relating to privacy, but that’s why this system is (for the most part) built around publicly available ‘OpenSocial’ information
    • Combined these tools (along with the network node targeting) certainly have the potential to start monetizing social networks better and deliver tighter, more targeted traffic to advertisers. It can also facilitate targeting of other content, suggest groups and users to members and more

Posted from Diigo. The rest of SocialMedia&Marketing group favorite links are here.


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