Archives for posts with tag: network

With the ominous SOPA act looming menacingly over the internet it is more important than ever to seek out and support progressive methods of getting artists and writers the money they deserve.

A stand-out service that I have joined is a social micropayment service called Flattr.  You create an account, choose a monthly amount of money to add to a pot (minimum 2 euros) and then click the Flattr button on webpages you like to share the money with the authors.

Kind of like tipping – the idea is simple, brilliant and completely in line with the ethos of the internet. I’ve recently noticed the Flattr button on a few websites – and I’ve started looking out for it on articles that I have enjoyed reading. It is a great way to reward bloggers for their hard work.

The service was started by Pirate Bay founder and spokesman Peter Sunde as a way to reward content creators for their work. Ambitions involve using the Flattr button to pay music and video creators as well as writers – Flattr has already teamed up with SoundCloud to include a Flattr button on their music player and there is a way to add a button to your Flickr account. YouTube are apparently keeping an interested eye on the project and Facebook are looking into delivering something similar. The service has already been used at conferences, enabling listeners to ‘Flattr’ speakers.

The Flattr team have already developed an app for Chrome that allows you to support Wikipedia by pressing a browser button whenever you have enjoyed or benefited from a Wikipedia article. As it is unofficial – they are keeping hold of the money raised and will deliver it the the Wikimedia foundation when enough money is raised. Also, when PayPal and Mastercard froze Wikileaks account – Flattr provided a way for supporters to send funds.

Flattr is a great project ran by people that really seem to value internet freedom over profit. It is a refreshing idea in an age of pay-walls and dangerous legislation, and it harks back to the democratic and collaborative origins of the internet. Money goes direct to the producer, the consumer decides what they consider a fair amount to pay and the Flattr button integrates snugly next to the Facebook ‘like’ button. It’s an idea I hope spreads – so sign up and start Flattr’ing.


You may be under the impression that when you search for something on Google the results you see are the same as anyone else that performs that search. This isn’t the case, and hasn’t been for a long time.

In 2009 Google went full steam ahead with personalized search. The idea was to look through your internet history, your Gmail and all the rest of your Google products and look for signals that would enable Google to tailor a search results to exactly what you are looking for.

As well as looking through your history, Google has always wanted to look at your social network to make your search results more relevant. The only problem with that is it doesn’t own any social network data – a social network like Facebook is a ‘walled garden’ that Google can only peek in from the outside.

The arrival of Google+ allows Google free-rein over your social data and will herald the age of a new buzzword – social search. Social search is the process whereby your social network (or social graph) affects the results of a Google search. By looking at the content that has been created or shared by people in my social graph, the results I get from a Google search will be more personalized than ever before.

I’ve already seen this in action. After searching Google for ‘SOPA’ (the Stop Online Piracy Act) I found myself reading from a website that I had never heard of. I traced how I ended up on this particular page and it turns out that someone I have in my Google Circle network was a writer for this website and had +1’ed the article.

This is great, right? Google search results will become more relevant, based upon people like me and less likely to be manipulated by dirty SEO tactics. Some people have even gone so far as to call this a ‘Socratic Revolution’ – suggesting that the era of personalized search is akin to the philosopher Socrates placing man at the center of the intellectual universe.

There is, however, a dark side to personalized search that has been recognized in a book called  ‘The Filter Bubble’ by Eli Pariser. The problem, he argues, is that this personalized ecosystem of knowledge acts as a mirror that reinforces what we believe without allowing the possibility of our views being challenged. Each new layer of personalization strengthens the walls of our own bubble – satisfying us with the information we want to see instead of offering new ideas. Or as he puts it, we are being given ‘too much candy, and not enough carrots.’

Whilst the Filter Bubble emphasizes our uniqueness, it acts as a centrifugal force – it pulls us apart from one another. With enough personalization the front page of Google News will be different for everyone, removing the kind of shared experience we used to have with a newspaper. Also, the Filter Bubble is invisible – we don’t know the maths behind how these algorithms define us. And with the increasing omnipotence of Google – it is difficult to not be a part of it.

So the arrival of Google+ social search marks a new era of ‘invisible autopropaganda’ that will continue ‘indoctrinating us with our own ideas’. What it will also mark is the start of a new form of marketing and campaigning – especially in the run-up to the 2012 US election. If I tap ‘Healthcare’ into Google I will be presented with the healthcare articles that my network has shared. Both the Democrats and the Republicans will have to fight to ensure that they have the right people inside the voters Google Circles.

Whilst we may still be at the dawn of social search – the correct techniques in this area could eventually make or break a campaign. Could 2012 be the year that Obama leverages Google+ to win the election?

The last few years have seen the adoption of social networking increase rapidly. From Facebook to Twitter,  LinkedIn to Flickr – there is a social network for just about anything.

As the revolution of social networking continues unabated, there comes a growing need to explore patterns within the networks – a process called social network analysis (SNA)

Previously, the world of social network analysis could only be accessed with a bit of computing knowledge. However, an open source programme called Nodexl has changed that by bringing some of the important metrics used to understand a network, and the ability to create impressive network graphs, into Excel.

Nodexl makes understanding a social network graph easy for anyone who can navigate around a spreadsheet. Excel is often where the world of computer programmers and the rest of us can meet up and speak the same language. Nodexl also makes it easy to import data from existing social networks such as Twitter, Flickr and Youtube

The people that can begin to make use of network graphs range from marketers to activists – and I imagine they are now a staple of any well equipped social media political campaign. Using a social network graph you can (among other things):

  • Spot the trusted influencers in a network
  • Find the important people that act as bridges between groups
  • Uncover isolated people and groups
  • Find the people who seem good at connecting a group
  • Plot who is at the centre and who is at the periphery of a network
  • Work out the where the weakest points of a network are
  • Assess who is best placed to replace a network admin
There are two basic components of a social graph:
  • Node: In a social network a node will usually represent a single person – but it can also represent an event, hashtag etc
  • Edge: A connection/interaction between two nodes – such as a friendship in Facebook, a follow on Twitter or an attendance at an event or Twitter Hashtag.

One major question that a social network analysis asks is how connected nodes (or people) are. But what determines how connected any person is? What metrics can be used to work it out how influential or powerful any individual player is?

These are some of the major metrics used in Nodexl – and they offer a good way to start thinking about your own networks:

  • Centrality – A key term which refers to how ‘in the middle’ a node is in a network.
  • Degree centrality – a count of the number of nodes a node is connected to. This could be the number of people that follow you on Twitter, or the amount of people that viewed a YouTube video. It is important to remember that a high degree score isn’t necessarily the most important factor in measuring a nodes importance.
  • In Degree and Out Degree – A connection between two nodes can be undirected (we are mutual friends on Facebook) or directed (you follow someone on Twitter that doesn’t follow you back). The In-Degree refers to the number of inbound connections, and Out-Degree refers to the number of outbound connections.
  • Geodesic distances – A geodesic distance is the shortest possible distance between two nodes  (popularly known as the degree of separation). In social network analysis, a nodes shortest and longest geodesic distance is recorded (the longest possible distance between a node and another is sometimes refered to as its eccentricity and can be used to work out the diameter of a network). An average geodesic distance of an entire network is worked out to assess how close community members are to each other.
  • Closeness centrality – This metric determines how well connected a node is in the overall network. It takes into account a nodes geodesic distance from all other nodes. Using this metric you can find people that don’t have strong connections.
  • Betweenness centrality – A score of how often a node is on the shortest path between two other nodes. This can be thought of as a bridge score – how important a node is at bridging other connections. People with a high betweenness centrality are often known as key players. A node could only have a degree centrality of 2, but if those two connections bridge to large unconnected groups, then that node will have a high betweenness centrality.
  • Eigenvector centrality – This looks at how well connected the people you are connected to are. It scores how much of a network a node can reach in comparison to the same amount of effort enacted by every other node in the network.

I am going to be exploring social network analysis over the next few weeks and blogging what I find here – if you want to follow along make sure you follow me on twitter or subscribe for updates.

The EOL is aiming to be a single online resource cataloguing all life on this planet. Collaborating globally with many other collections, the site is working to provide a webpage for every single one of the 1.9 million species on the planet.

Each page will contain photo’s, sound-clips, videos, maps and articles written by experts and verified by the scientific community. There is also prominent ‘threat status’ section, letting the viewer know how endangered the species is.

On every page there is a dedicated community page which links to all discussions that relate to that particular species. Anyone can sign up and begin a discussion and all content is licensed under creative commons. It currently has 48,000 members who have already contributed towards the 634,000 images on the website.

The ultimate goal is to ‘make high-quality, well-organized information available on an unprecedented level.’

This infographic demonstrates how understanding the scale of involvement in social media can help distinguish and categorise different social networking systems.