Social Network Analysis (SNA) is a tool for mapping / visualizing relationships between individuals. SNA was developed to understand the relations (ties/edge) of the actors (nodes/points) that exist in a system with two focus, namely actors and relationships between actors in a particular social context. This focus helps in understanding how the position of existing actors can affect access to existing resources such as goods, capital, and information. Information is one of the essential resources or resources flowing in a network, so SNA is often implemented to identify the flow of information.
One method that must first be done before analyzing a network is crawling. Crawling here aims to retrieve and gather information from several connections to be further analyzed in the form of social network analysis. And here, I will browse the list of followers and following my friend’s Twitter account, Haydar, to find out the ego network of Haydar’s social network.
The first thing to do in making SNA is through crawling with R studio tools and this is the script to crawl (collect) follower data and folowing data @haydars_alfathi
After that, we will get a raw CSV file which will then be simplified into edges and nodes files for further analysis and visualization through the Gephi application.
After the edges and nodes table is created, it will then be analyzed and visualized through Gephi software. This software is one software that is often used in Social Network Analysis. I will here analyze the ego network of the @haydars_Alfathi account to find out the relationship of the account with its social network.
This is a form of visualization of the SNA ego network from the @haydars_alfathi account, where we can analyze the attributes of SNA, namely the betweenness, closeness, degree, and modularity of the SNA. Based on these data, I use resolution modularity of 0.49 to form 4 groups because Modularity measurement can also be interpreted to indicate the number of groups that will be formed using
the clustering coefficient. And can also be seen the highest betweenness is in the account @haydars_alfathi, which means
it has a high potential in manipulating the outcome of a network (the quality of collaboration between networks. Moreover, the highest closeness also exists on the @haydars_alfathi account, which can indicate the speed of information dissemination. The greatest degree of both degree and out-degree is also on the @haydars_Alfathi account. The higher the degree value of a node, then more and more individual acquaintances have represented
node or can be called a key player
Fakhri's note 