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.