- RT @MLuczak: Related papers: dl.acm.org/citation.cfm?i… dl.acm.org/citation.cfm?i… dl.acm.org/citation.cfm?i… peerj.com/preprints/2789/ #webscience #w… 6 days ago
- Interested in the emergent properties of social machines? My #WAIS seminar on the research in @project_sociam youtu.be/DBSe6k8fFkQ 1 week ago
- RT @MLuczak: "What an entangled Web we weave" preprint at peerj.com/preprints/2789/ #webscience #datascience #networkscience #openscience /cc… 2 weeks ago
- Speaking today about Citizen Science and real-time communication @ICWSM2017 #ICWSM2017 #eyewire eprints.soton.ac.uk/406181/ 2 months ago
- We can look backwards at the Web, but we need to look forward in order to make it what we want @susanjhalford… twitter.com/i/web/status/8… 3 months ago
A Blog recording the life a Web Scientist
CeBIT Day Three Twitter Analysis
March 8, 2013Posted by on
As before some key facts about the Twitter conversations at CeBIT during the past 3 days:
- 38033 Tweets analysed
- 18428 unique users tweeting using the #CeBIT hashtag
- 7652 retweets made
- 4788 tweets containing mentions
As before we are seeing similar spikes in the communication activity during the day, with a number of key announcements and events proving to be important enough to cause a sudden rise in activity, indeed, the announcement of the real-time cybercrime mapping by Deutsche Telecom was a popular topic within the Twitter communications. The news of the top ‘Nine online marketing trends’ was also popular, but not only in terms of just announcing the news, but also causing communications between different users (in terms of mentions).
As before, we are seeing the influence of specific users within the network continuing to grow, namely ‘Cebit’, ‘Code_N’, and the disconnected conversations of ‘Nienor_’. As before, we are seeing users taking the role of drawing together these different streams of information from these highly retweeted users, such as ‘eklaus’, or ‘CeBIT_Japan’.
Taking another look at the network, we are also able to explore the flow of conversations, or simply, how the tweets were retweeted overtime. As the Figure below illustrates, the speed of diffusion of the tweets and their size differ dramatically (only chains of larger than 20 are shown), with the vast majority of them (99.6%) forming chains of less than 3 retweets. Does the content of the tweet or the person that it originated from depend on the speed and size of the chain? More to come on this later!