donderdag 1 december 2011


Journalists following other journalists on Twitter.
In the network society our communication has fundamentally changed. We are in contact with people when we know an e-mail address, Twitter or Facebook name, or LinkedIn handle, even if we never have met these persons in real time. In the more traditional society of the past century our contacts were limited to people we know face to face from the circle of family, neighbors, 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, some are friends of friends etc. The patterns of communication in such networks are interesting to study. Social network analysis has become a fountain of empirical knowledge, not only for social scientist but also for datajournalists, because there is a enormous amount of data free available and the tools for the analysis are becoming more easy to handle.
Published on Memeburn:

Social Network Apps
Recently an Italian study 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 then smaller. The Guardian produced an interesting graph of Twitter contacts between UK journalists, showing “that 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 a social network analysis. Facebook offered an app called Friendwheel, which displays your friends and their friends in a full circle. Facebook Visualizer is an other one for displaying the structure of your Facebook network. For Twitter you can do the same with Twitter friendwheel. For Twitter there are a tons of applications and some of them dig a bit deeper into your network. Mentionapp showing a network of mentions on Twitter and, Klout score giving 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. However, as with a lot of tools in the box of scientists, most of them can now be used by the public as well. For social networks analysis NodeXL is one of the most easy to use tools, Gephi is an other one. 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 operation 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, it is free for download and has a good manual and examples to get started. It works perfect 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 are giving a deeper insight into the network structure than the ordinary Twitter and Facebook tools.

Political Network
Attracted by the simplicity I decided to give NodeXL a try and started analyzing the Twitter network (following each other) between politicians and reporters at the Dutch Parliament in The Hague. The results show that the selected 150 persons had 5000 relations in common, with a maximum distance of 4 and average of 1.6 degrees. The density was .22. Meaning that the members of this Twitter network could in two steps connect. But there is no power-elite; that is a fully connected network between journalists and politicians because only 22% of all possible connections were realized1. Although one expects politicians to be prime sources; it was found that a journalist, from the commercial media networks, was the leading source. Twitter was more used by 'post-modern' political parties (for example the Greens or Neo Liberals) and not by socialists, however one of the top networkers (building bridges between parts of the network) 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 appeared that journalists and politicians are no connected according to ideological or religious lines but that news was the driving force behind the network connections.

A more tradition journalistic approach based on interviews reveals also interesting findings about the relationship between journalists and politicians, however this more science based approach in the directions of datajournalism shows the structure of network. 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 in 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”.

1The results of this research were presented at the conference about the 'Future of Journalism' at Cardiff in September 2011. The full article will be published next year in Journalism Practice. A Powerpoint presentation summarizing the results is on my blog.

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