#nhschangeday 3rd to 5th July 2014through the lens of graphical Social Network Analysis using

NodeXL

A very basic introduction with example graphs (and lots of caveats)

Mark OuthwaiteOuthentics ConsultingHelping find direction through complex times

Phone: +44 (0)2032399438Mobile: +44 (0)7768131770Skype: markout

Website: www.outhentics.comMore about me: http://www.linkedin.com/in/markatouthenticsFollow me on Twitter: @mark_outhwaiteBlog: http://outhentics.blogspot.co.uk/To schedule meetings with me go to: http://doodle.com/outhentics

Introduction

• This presentation provides a brief and very simplified introduction to the techniques used to analyse the Twitter activity taking place during the NHSCHANGEDAY online event which took place on 4th July 2014 – Social Network Analysis (SNA)

• Graphical SNA allows you to explore and discover the connections and patterns between people whether they are in organisations, talking across organisational boundaries or in social settings. Who are the ‘hubs’ and ‘bridges’, the experts and the lone voices? How do the actual patterns of conversation actually contrast with the expected patterns? How do networks evolve over time and what is their response to change.

• An oversimplified introduction to the concepts involved is included but I strongly recommend that you review these two sources for a much better introduction and an excellent illustration as I am just a beginner.

– http://www.slideshare.net/Marc_A_Smith/2013-nodexl-social-media-network-analysis– http://www.pewresearch.org/fact-tank/2014/02/20/the-six-types-of-twitter-conversations/

• The NodeXL software used is Open Source and freely available for download for use with Excel 2007 onwards– http://nodexl.codeplex.com/

• NodeXL can take data directly from a wide variety of sources including Twitter, Facebook, YouTube email, Exchange servers and a variety of other sources as well as data captured through other routes such as part of organisational SNA surveys (eg – name the 4 people whose advice you most value)

• The data source in this case is a Twitter search using the NodeXL import function– Search was for all Tweets containing #nhschangeday ‘since:20014-07-03’ and was conducted on 06 July

2014– You should be aware that due to Twitter API limitations on external searches that it is possible that not all

Tweets meeting those conditions were retrieved– Twitter searches are normally only possible for a maximum of the previous 7 days activity. So if you want to

monitor and map a conversation over an extended period you will have to schedule regular searches and downloads

• If you want a copy of the data for your own use in NodeXL or you use another package (eg GEPHI) please let me know.

• If anybody feels the need for an opportunity for a further discussion about this presentation or the other uses of graphical SNA then please let me know – I am happy to host an online live webinar (GoToMeeting). If you are an expert in the area and would be prepared to talk on the webinar even better.

• Continue the conversation on #nhschangeday

Connections and patterns in Twitter

• ‘I Tweet therefore I am’ – unless you tweet, retweet, reply or mention we do not know you exist.

• An individual, for example ‘@mark_outhwaite’, is described as a node

• If an individual is mentioned in a Tweet by another person or vice versa then a relationship or ‘edge’ is created between the pair.

• The relationship is ‘directional’ depending on who mentions who in their message.

• The ‘edge’ or connection gets stronger/closer as there is more activity between the nodes.

• If I tweet and there are no forwards, mentions, replies, quotes or favourites then there is no relationship

• A cohesive group or sub-group is created when there is a well connected group – lots of ‘edges’ connecting the members of the group and many of those being strong connections.

• Centrality – the number of direct (normally incoming) connections an individual has to other members of the group. So if @mark_outhwaite receives lots of mentions from the same people then he will appear closer to the centre of the group.

• Betweenness – a measure of how much of a bridge you act as between other people. For example you may be a bridge from your group to one or more people in another group, or between smaller clusters within your group

SNAPSHOTS FROM THE ANALYSIS

These are just some static snapshots from the data.

NodeXL has dynamic filters which allow you to explore the data in more detail live on screen.

The full picture of #nhschangeday

This is the full picture of all activity during

the period.

Groups are created based on the intensity

and type of relationships between

participants.

The words most mentioned by the

groups are shown in each group box.

The edges are ‘bundled’ tightly to keep the map tidier.

Who are the main bridges between groups?

We use a measure of ‘betweenness

centrality’ to identify individuals who are

most likely to be acting as bridges or information brokers

between the different groups.

Who has more than 2000 followers?

Here we pull out those people with the most followers. In this

case 2000 or more followers.

This means that it is these people whose

tweets have the most reach.

In the case of nhschangeday there will be many people who read the tweets but did not retweet,

reply or mention

Who mentions a lot of other people in their tweets >=10 other people ‘namechecked’ in their tweets

There will be some people who mention a lot of other people

during the period. They may be doing a lot of retweeting of

other people’s tweets or holding a lot of

conversations (mentions) with

others in their groups.

Who gets mentioned a lot in other people’s tweets>= 10 mentions by other people

Some people get mentioned a lot in

other people’s tweets – normally because

they get retweeted a lot.