Large Language Model ChatGPT has received an upgrade in the form of voice and image capabilities. OpenAI confirms that the language model offers a…
The nature of communication has fundamentally changed in the age of the network society. We are in contact with people via an email address, Twitter or Facebook name, or LinkedIn handle, even if we never have met them in real life. In the more traditional society of the past century our contacts were limited to people we knew face-to-face from our circle of family, neighbours, colleagues, and friends. Now our contacts are in a huge network of connected nodes.
Some we know face to face, some we only mail or follow on Twitter, others are friends of friends …you get the picture. The patterns of communication in such networks make for an interesting study. Social network analysis has become a fountain of empirical knowledge, not only for social scientists but also for datajournalists, because there is now an enormous amount of data freely available and the tools for the analysing it are becoming increasingly easy to handle.
Social Network Apps
An Italian study recently reported that Facebook users are, on average, separated by 3.74 degrees, meaning that in about four steps one Facebook user could connect to another. In 2008, the degree of separation was 4.28, and the Facebook network was much smaller back then. The Guardian produced an interesting graph of Twitter contacts between UK journalists, showing the “journalists follow other journalists, mostly from their own organisation”. The American website Muckety maps the paths of power and influence based on network connections.
There are simple tools to start with, if you want to get going on a social network analysis. Facebook offers an app called Friendwheel, which displays your friends and their friends in a full circle. Facebook Visualiser is also great for displaying the structure of your Facebook network. For Twitter you can do the same with Twitter friendwheel. On Twitter, there are tons of applications and some of them dig a bit deeper into your network. Mentionapp shows a network of mentions on Twitter, and Klout score gives you some idea about your influence in the network.
All these tools are based on what is called the structure of the “ego-network”; that is, the network of your friends and their friends. Of course it helps to understand the structure of the network, but the results are limited. Switching to more sophisticated applications from the social sciences, like UCINET and Pajek for analyzing graphs, is difficult, because these applications have a steep learning curve.
As is the case with a number of tools in the box of scientists, though, most of them can now be used by the public as well. NodeXL is one of the most easy to use tools for social network analysis. Gephi is another one. Straight up Wikipedia gives an overview of all the different software programs for social network analysis.
Gephi, based on Java, is open source and can be used on any operating system. The visualisation of the Egyptian Twitter revolution is an interesting example of the use of this software. NodeXL is a template for Windows Excel. It is a complete tool for social network analysis, free for download and has a good manual and examples to get started. It works perfectly in combination with social media because data can directly be downloaded from the network-for example Twitter-into the program. But it also works for e-mail, web pages, Flickr, YouTube and Facebook.
Once you have the data, the program produces graphs of the network and calculates the most important centrality measures. These measures, in combination with the graph, give a deeper insight into the network structure than the ordinary Twitter and Facebook tools.
Attracted by the simplicity I decided to give NodeXL a try and started analysing the Twitter network between politicians and reporters (following each other) at the Dutch Parliament in The Hague. The results show that the 150 people selected had 5 000 relations in common, with a maximum distance of four and average of 1.6 degrees. The density was .22. This means that the members of this Twitter network could connect in two steps. But there is some level of discord, because only 22% of all possible connections were realised.
Although one expects politicians to be prime sources; it was found that a journalist, from a commercial media network, was the leading source. Twitter was used more by “post-modern” political parties (for example the Greens or Neo Liberals) and not by socialists. One of the top networkers (building bridges between parts of the network), though, was a socialist. The well known right wing nationalist Geert Wilders, did not follow anybody and used Twitter for broadcasting his anti-Islam ideas. Finally, it appears that journalists and politicians are not connected according to ideological or religious lines but rather that news was the driving force behind the network connections.
A more traditional journalistic approach based on interviews also reveals interesting findings about the relationship between journalists and politicians. This more scientific method of datajournalism, however, shows the structure of networks. Philip Meyer is one of the founding fathers of Computer Assisted Research and Reporting (CARR) and Precision Journalism. He was the first to use an IBM mainframe for reporting about the Detroit riots in the US during the sixties. Recently he said that the aim of journalism is of course to help democracy and inform the public; “Narrative journalism combined with precision journalism could do that job. Let’s get started”.