In 2009, Google struck a deal with Twitter, rumored at $15 million, to integrate tweets into keyword related Google searches. And last month, Google also integrated real-time search technology to surface blog posts and news content as they hit the Web – dramatically improving the previous five to 15 minutes its spiders would take to crawl the Web. I should also note that Collecta also offers the ability to search the real-time Web, but its results also include popular networks within the social Web. Between Google and Collecta, Twitter Search is starting to show its age.

The opportunities and benefits of accessing the real-time Web also represent its most notable deficiencies – the ability to truly focus the stream of cascading information into a river of relevance. Companies such as My6Sense are using a form of “digital intuition” to escalate tweets that match our patterns, behavior, and content we read.

We are now staring in the face of a more sophisticated era of real-time search that will further advance, localize and personalize over time. And, everything starts with the experimentation of sophisticated algorithms that filter and rank the content we’re hoping to discover.

For example, Google recently adapted its PageRank technology for surfacing related tweets. PageRank was originally developed to help find relevant Web pages through traditional search and was Google’s primary differentiation in a world of commodity search platforms. Essentially, Google’s PageRank assesses the importance of Web pages tied to keywords based on link structure. Authority is determined by the quantity and quality of inbound links to each page as well as the branches of outlying link relationships that tie other pages to those within the first degree of inbound connections. In other words, the more links to a page and the more linkers to each link, the greater the value of the original page.

The challenge with real-time search is tying tweets or other social content to notable producers and their networks of reputed followers and sub-follower architectures.

In an interview with Technology Review, Amit Singhal, a Google Fellow who led development of real-time search, said “You earn reputation, and then you give reputation. If lots of people follow you, and then you follow someone–then even though this [new person] does not have lots of followers, his tweet is deemed valuable because his followers are themselves followed widely.

As Singhal emphasized, “It is definitely, definitely more than a popularity contest.”

Google also examines the signal in the noise, to surface the most relevant tweets related to common as well as obscure subjects. And as Twitter itself advances the technology that packages tweets, such as geo-location data, we can expect to see a rapid evolution of real-time search.

Basically, a follower is the equivalent of one page linking to another on the Web. Google recognizes each as a form of recommendation. So as higher quality pages link to sources, the original page increases in value. In the Social Web, reputed users who follow other users inherently increase the stature of the individual to whom they connect.

Searching for a particular keyword now will produce qualified results for Web pages and also content published in Twitter and other social networks, ranked by the authority of the page and publisher of social objects as assessed by PageRank technology.

In the eyes of Google, the adaptation of PageRank for Social Media essentially creates a human algorithm or a PeopleRank of sorts that may eventually serve as a foundation for also assessing the authority of an individual in the social Web.

Other companies are also introducing new services to measure general authority for individuals online. Klout, for example, developed a sophisticated platform for measuring the influence of users in Twitter. Based on three sophisticated stages of semantic calculation (True Reach, Amplification Probability, Network Value) Klout can determine not only the level of influence of any user on Twitter but also the most influential voices tied to topics or keywords. Microsoft’s search engine, Bing, is also including tweets in its real-time search feed and could, for instance, integrate Klout’s influence engine to rank tweets and other social objects to qualify results.

But while the idea of ranking influence on the social web is interesting and necessary, it is far from perfected. Running searches in either engine today will only reinforce this sentiment. However, with that said, it is helping us by reducing the obstacles that typically prevent or prolong the process of finding pertinent information. It will only improve over time regardless of our personal views on establishing a hierarchy of people in social media.

As the human algorithm continues to evolve, it transforms the definition of and logic for relationships. We’re adapting how we connect to one another and also constructing new roads for sharing, filtering, and ranking relevant social objects. The ties that bind us now serve as the source of how we discover information and also how it finds us. And as such, the relationships we maintain on the Social Web determine the ranking of the content we produce and its place within the social hierarchy of search results.

Perhaps the next iterations of Search Engine Optimization (SEO) and Social Media Optimization (SMO) will focus on enhancing the link structures of human relationships to escalate the prominence of our stature and the social objects we create and share.

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