After visiting the course ‚Networks, parties and associations’ I was really looking forward to do a first network analysis on my own. The aim of this analysis was to get familiar with theories and techniques of network analysis and to relate the topic of gatekeeping to this method of social analysis. In order to answer the question if gatekeeping still exists in social networks I decided to visualize and interpret the network of twitter users from Friedrichshafen. In the following I will describe my approach very detailed and offer my data set for anybody who is interested in doing a network analysis on his own.
Gatekeeping is a well discussed topic in communication science. While in former ages media like newspaper, radio and TV was the only source of information about the world it had a very powerful position as ‘keepers of the informational gates.’ As it is well known this has fundamentally changed by the internet and is still in a process of constant change. Nevertheless in my point of view it is not very wise to think that this means that gatekeeping has vanished with some sort of “swarm intelligence” in a network society. I wanted to analyze if in modern information networks some kinds of gatekeeping could still be found. As this is a very big question I limited myself in two ways: Firstly I chose twitter as the perfect source for my data collection as it shows nearly every connection between the users. Secondly I decided to regard only twitter users which are related to Friedrichshafen.
The first big problem in doing a network analysis is to set the right border of a network. As it is well known nearly everybody is connected with everybody somehow through six connections (Milgram 1967). Even if this isn’t the case on twitter finding the right amount of actors is difficult and important as well. I decided to do it as follows: I searched for twitter users which have ‘Friedrichshafen’ in their self description and found 23 actors. After this I collected the followers of all of these 23 ‘core actors’ and analyzed which followers are following how many of the core actors (Sheet 1 in the data set). My idea was that everybody who is following more than one of the core actors could be regarded as interested in the topic of Friedrichshafen. But as the relations between the single actors are directional and thus an adjacent matrix for the network analysis is needed my approach had a big disadvantage: The needed data was growing potentially with every new actor. As a network with 600 nodes leads to a matrix with 360,000 data entries a way had to be found to set a manageable amount of actors in the network. Because of this only those Twitter users were taken into considerations which were at least followers from five of the 23 core actors. This led to a list of 67 actors which was combined with the core actors what resulted in a list of 82 actors (8 actors were doublets or restricted access to their list of followers). This list was the final data basis for the network analysis (‘Final Matrix’ in the data set).
So if you now ask how I collected the data for a matrix with 82² data entries I can tell you that being a programmer would have made it much easier. I copied the content of every follower page into MS Excel, extracted the names (as they could be found in a regular interval of cells), compared the extracted names with the list of my 82 actors (SVERWEIS) and copied the lists into the final matrix.
During the comparison of the different relations between the actors two other kinds of data were collected. Firstly the number of followers as this number gives a hint of the informational range in the global twitter network. Secondly was every user assigned to one of ten groups: business, media, NGO, public, party, private person, red light, association, science and unknown (see Table 3). Last but not least the data was uploaded to Visone a program for graphical network analysis as a “one mode” network.
After this several different values of network analysis could be computed and visualized. The most interesting one is the outdegree as it is the perfect measurement of influence within a twitter network. In order to understand this you have to keep the following in mind: When one actor is sending information to another actor he doesn’t do it on purpose. On Twitter messages are generally sent to everyone and nobody – the reach depends on the people which decided to follow this specific user. Therefore it cannot make sense to take received messages into account for a centrality measurement. Some users are following countless other users only with the aim to rise the own amount of followers. Because of this the outdegree has to be used in order to visualize the adequate centrality of the nodes.
Regarding the picture of the network one thing is getting obvious at a glance: There is one influential node right in the middle of the network which means that this node has a big influence even if this actor hasn’t such a big number of followers (expressed by the size of the node). In fact the actor in the centre is the twitter account of ‘Schwäbische Zeitung’ and stands for the group of the old media. It is obvious that this can’t proof that in the Twitter network of Friedrichshafen old gates still exists. On the other hand it is interesting to see that private persons and associations are playing in the network a minor role and can’t therefore set their topics on the agenda as easy as the old media.
I don’t want to go further into detail by now but want to invite you to use my collected data for your own first steps in social network analysis. If you have questions or recommendations, feel free to leave a comment.
About gatekeeping and the new role of journalists:
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About network society and network analysis:
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