In this post we take a look firstly at the @ddjjournalism Twitter account, and then the #ddj hashtag. Data Driven Journalism (ddj) is a fantastic resource, and has a number of excellent articles related to that of social media. It is a hub for news and resources from the community of journalists, editors, designers, and developers who use data in the service of journalism. Check out an article on NodeXL that was published on the ddj which can b found here.
The visualization looks great, but you may be asking, what does this all mean?
The top left hand side of each group is labeled e.g. G1 refers to ‘Group 1’ and G2 refers to ‘Group 2’ and so forth, and the keywords relate to the most frequently occurring per each group. NodeXL network graph reports also produce a number of other metrics such as the most frequently shared URLs, Domains, Hashtags, Words, Word Pairs, Replied-To, Mentioned Users, and most frequent tweeters These metrics are produced overall and also by group of Twitter users. By looking at different metrics associated with different groups (G1, G2, G3 etc) you can see the different topics that users may be talking about.
We can also take a look at the 6 types of network structures from Smith, Rainie, Shneiderman, & Himelboim (2014) as a guide in interpretation.
Figure 2 ‘Six Types of Network Structures’
Looking back at figure 1, we can now see that many of the groups resemble a broadcast network. That is, users are sharing articles that have been published on the website, and their followers are retweeting these.
Next we decided to take a look at the hashtag ‘#ddj’ to examine some of the top influences and popular content associated with data driven journalism.
Figure 3 Network graph of ddj (full report here)
We can then take a look at some of the top influences (ranked by betweenness centrality):
Figure 4 – Top influencers ranked by betweenness centrality
The figure above displays a number of influencers, ranked by betweenness centrality, related to the hashtag ‘#ddj’, which includes YouTube, the Financial Times, and individuals sharing ddj related news.
Figure 5- Most frequently occurring URLs
The data driven journalism resource may be interested to know that their survey which sought information on the current status of data journalism was among the most popular URLs that were shared within the previous week or so.
Figure 6- Days and hours of the week most active
As the figure above shows the most active day for the @ddjjournalism account is that of Thursday, and the most popular time for sharing content is 10am.