This is a topic I keep reading about – with a lot of opinions

The question here is about filtering all the noise to get you what you want…

If trust, reputation and authority are keys to digital filters then Brands should win and therefore Google will favour them.  Back however to the question about who writes the algorithm and code and how are they personally biased towards what they favour?

If the web is a "Social" something then this equals Facebook, Xing, Twitter, LinkedIn Google+. 

But social could mean....

"see what your friends are searching, buying, watching, liking, saying or doing"

"buy together and recommend

"filtered by who you know"

"what's trending"

"where are my friends right now or where will they be"

Given that a social graph is a digital map that says, "This is who I know." It may reflect people who the user knows in various ways: as family members, work colleagues, peers met at a conference, high school classmates, fellow cycling club members, friend of a friend, etc. Social graphs are mostly created on social networking sites like Facebook and LinkedIn, where users send reciprocal invites to those they know, in order to map out and maintain their social ties.

And an interest graph is a digital map that says, "This is what I like." As Twitter's CEO has remarked, if you see that I follow the San Francisco Giants on Twitter, that doesn't tell you if I know the team's players, but it does tell you a lot about my interest in baseball. Interest graphs are generated by the feeds customers follow (e.g. on Twitter), products they buy (e.g. on Amazon), ratings they create (e.g. on Netflix), searches they run (e.g. on Google), or questions they answer about their tastes (e.g. on services like Hunch).

However where is the value? It has to be in the mashup/ combination of all social data so I can determine who influences you and who you influence -  where is your power and how much you are worth to a brand.......

Two insightful articles from Michael Wu

 Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics and its application to Social CRM. He's a regular blogger on the Lithosphere's Building Community blog and previously wrote in the Analytic Science blog. You can follow him on Twitter at mich8elwu.

Quantifying Facebook Engagement: More than Just Counting Fans and Likes

Deeper Facebook Engagement: Dissecting Interactivity

Conclusion…..

It is possible to understand how fans engage within a fan page by looking at thread depth, the unique number of other fans they interact with, and the dynamics of these interactions. By doing so, we covered three more levels of engagement metrics. So our spectrum of engagement is five levels deep now.

 Level 0: Total fan counts

Level 1: Active fans

Level 2: Interactivity through comments

Level 3: Thread Depth – amount of interaction

Level 4: Unique fans per conversation – with how many other fans?

Level 5: Average/median response time – dynamics of interaction

 The Level 3 (thread depth) and Level 4 (unique fans) data suggest that most of the fans only post once within any conversation. We will see if this is indeed the case next time. This is a very important point, and we will discuss some of its consequence in subsequent posts.

 Although we are already at Level 5, we are not yet at the bottom. There are three more levels to go! In subsequent posts, I will reveal more data on the deeper levels of engagement. But for now, let me know what you think about this spectrum of engagement metrics. As usual, kudos, comments, suggestions, critiques, and discussions are always welcome. Stay tuned for even deeper level of engagement...

PageRank is the foundation of Google, but doesn’t do social very well. With Google Plus will we see a SocialRank emerge?  Now this is not new either for Google or for the likes of PeerIndex or Klout who have already added many sources of data for its rankings and are fighting to become the default measure of online influence, something that advertisers and marketers in particular are extremely interested in as they try to identify “influencers” who can spread their messages

SocialRank will be based on Digital Footprint data that comes from Blog, Google +, Twitter, Facebook, Quora and LinkedIn accounts and one does expect the same underlying ideas of PageRank – insomuch that your digitalfootprint data comes from you and there is a need to balance this with data that others say about your or your data. Digital Footprint 101

Klout focus is on overall “reach” and “amplification” These are determined by looking at a user’s activity and how much impact it has on their social graph (whether their tweets are re-tweeted by others with influence)

Whereas PeerIndex looks at a user’s activity in and generates an authority rank for their expertise in 8 topics, which create an influence “footprint” for each user. PeerIndex does look for “realness,” you are a person and not an automated feed or “spambot”

Mashup applications and services are emerging on the back of SocialRanking. Some retailers are starting to offer perks to Klout users who have high scores based on the premise of access to “influencers”.  Peersquare  (PeerIndex and Foursquare) is ranking people who are in the same location as you, something that could be useful during a conference or other event.  Salesforce.com has even talked about compensating employees based on their influence within social networks, Think the Social CV

Google now has it own social silo in the form of Google +, but it doesn’t appear to be in the best position to use others social data if locked into the silo where Google cannot index it. So does Google plan is to kill off Facebook and get all social data in its own Silo combined with other open data, or will they give the data to the user.  So far Google has gone down the open route and you can grad all your data (Data Liberation) that is there and the privacy controls look good.

But if data is cheap and is the analysis is key, will someone elect to undermine Google by putting the algorithms into an open space?