CeBIT – Oz Day 1 Twitter Storm

Day 1 CeBIT Australia is now over and the twitter storm was intense. Some 1,244 tweets against the #CeBITAus tag. But what about the TwitChat? Only some 250 messages were actual interactions i.e. a mention/retweet or a direct reply. Storms are something to duck for cover from. However, amongst the tweet storm there are some gems that people think are important enough to mention to their followers….just 14% to be precise. We can learn a lot from this 14%. For example who is the most influential Tweeter based on the interactions that they provoke. We used the NodeXL software to import data from twitter to visualise who talks to who:

CeBIT TwitChatThe twitter profile pics are sized by the number of mentions or replies the tweeter has received. So the bigger the picture, the larger the impact their tweet/s have had on others. Here is our ‘league table’ of ‘most noticed’ tweeters for CeBIT day 1:
Twitter Name
Number of Mentions
Now we can learn a little more from the patterns of interactions from the maps. For example lets look at Lisa_Cornish’s direct interactions:

Cebit-CornishWe can see that 22 people have each mentioned Lisa’s tweet, so clearly it was the most engaging tweet of the day!

Now lets pick out someone else from the map and look at their pattern of interactions:

Cebit KcarruthersWe can see that ‘kcarruthers’ has fewer mentions than Lisa, but a bit of a ‘community’ developing around her. Lisa may have tweeted something very noteworthy, but I suspect that kcarruthers may have longer lasting influence through the community surrounding her. 

So those of you into CRM and Social CRM take note. Its not just the number of interactions, its also the nature of them!

Feel like exploring a little more? We have taken the NodeXL data and created an interactive version using our WebMapper technology. Be warned, it works best with Chrome, Firefox and the latest versions only of IE and Safari. Also only the newest IPads (IOS 6)

There is a lot you can do with the interactive map. Firstly you should play with the ‘expand’, ‘zoom’ and ‘font size’ sliders to explore how to visualise the map. The next thing you will notice is the different colouring of the nodes into some 20 groups. These were determined by NodeXL’s clustering algorithms which try to cluster nodes based on common connections. You can explore these by checking and unchecking them in the tick boxes. You can also check and uncheck the ‘mention’ and ‘reply-to’ links. Finally you can select a single node to see the network that surrounds just them (like we did with lisa_cornish and kcurruthers). Mousing over the node will expose their twitter handle. You can search for a twitter ID in the search box. ‘Show all’ will restore the original map.

Enjoy your explorations!

This entry was posted in Case Study, Social Network Analysis and tagged , by Laurence Lock Lee. Bookmark the permalink.

About Laurence Lock Lee

I am a co-founder of Optimice Pty Ltd, the developer of Community Mapper, ONA Surveys, Visual Markets and Stakeholder Engagement. I am also the Chief Scientist and co-founder of Tech startup Swoop Analytics. My passion is networks; building them, participating in them, analysing them, improving them, writing about them. My work experience has largely been with corporate networks. However, I am excited about the prospect of working with community and network builders from all walks of life, be they NGOs, charities, professional societies, industry peak bodies, social enterprises etc... It doesn't take long to appreciate that 'building community' is the new 21st century mission statement.