IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
9th TripleHelix: Politicians Twitter network - a case of S. Korea
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Notas del editor
The research on Twitter has been developing, especially with the relation between Twitter and Politics.I think previous studies could be categorized into two. The relation between public and politiciansIndividual level research focusing on personal behaviors of politiciansHowever, I’ve not seen a study on politician’s Twitter network
However, it is intriguing to see how politicians’ Twitter network can co-opt with previous research on politicians networkRegarding politicians’ network, the most well-known network is probably bill cosponsorship network,Which is indirect network based on bill legislation. In this case, the boundary of politicians to connect is based on set of people to sign rather than whole politicians.And we can also think of hyperlink network between politicians, which is mediated network by politicians’ hompages or blogs.Unlike the cosponsorship, it is direct link to other politicians and also public representation to the public. However, they lack the property that politician’s Twitter network has.First of all, Twitter network is the only network that the public and peer politicians are located in the same platform Therefore, second, the network boundary is totally different from previous two network.
To elaborate, this is a unique feature of Twitter networkSince politicians are embedded in the Twitter,Where, network boundary is fixed, like the existence of other politicians in the same media platform, they have a definite set of people to connect as soon as they enter Twitter space,So there might be a stronger social pressure to connect other politicians…… that leads us to see that twitter network can be a network of social ritual like saying hello to everyone.Another feature is that the co-existence of the public influence relationship between politicians on Twitter because they are within direct gaze of the public, which makes politicians to consider how their connections to be seen by public…. This lead us it could be possible that they want to show political support to popular politicians on Twitter.
So, we want to see politician’s Twitter network really different from previous studies on politicians’ Network and collected data.We have collected 189 politicians and divided them into two political groups or ruling party and oppsition parties because literally there were too many parties in South Korea.
In fact, dividing into two groups seems appropriate because our CONCUR analysis, which is block modelling technique to divide blocks, shows that it really matches with network-based group distinction.
The story goes like this.Since Politicians Twitter network has relatively fixed network boundary so that it gives more social pressure to connect other politicians, meaning the Twitter network will show higher network density than other types of media network (such as hyperlink network of blogs or homepages) and more reciprocity, which is bill cosponsoring network has revealed that the most important principle to connect.In addition, because of the public, politicians concerns how their connections to be seen so that they may connects politicians who are popular on Twitter, managing their impression and relation with other politicians.
Our result seems quite relevant to these stories. This is network density table.. (explanation…)
And this is cohesiveness of network table.. ( explanation)
Then what makes difference between the following-follower network and mention network?We conducted EGRM model to see the difference.The difference is that, as you see, reciprocity. Check this Reciprocity AAB .. (explanation)
Then, we saw that the following-follower network and mention network have different logics and the following-follower network is based on reciprocity.The question is what makes mention network different from the following-follower. It turns out that, as we put it, the more popular a politicians is on twitter, the more politicians mention to the politicians.(explanation of two variables.. )
It could be more intuitive to see through graphics.We depicted Politicians’ Twitter network. We have drawn the mention network over the following-follower network(explanation, if necessary)
The distribution between politician confirms this The following-follower network shows a linear function, meanwhile the mention network is more similar to power-law distribution.Reciprocity-based connections is, basically, “ I link to people who linked to me” .. So linear functionThe gravity to popular person is that “I mention to people who get most attention” So preferential attachment principle to connect…leads power-law function.