My Digital FootPrint

Image: Facebook CEO Mark Zuckerberg gestures during his keynote address at the Facebook f8 Developers Conference. Zuckerberg's Law is that every year the amount of personal things you will share on Facebook doubles – Source NYT 2008 there are three questions we are now facing in 2013 as sharing continues to grow and we hit the question of where we will end the year. Is there a finite amount that you are prepared to share about your life with others?; Is there a finite amount I am prepared to accept as shared?; and, Is sharing driven by us or the machine? The question is not when will it slow, but what will cause sharing to slow / change. However as sensor and medical data comes online will the  trend prove true?  

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 se...

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 ...

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...