Social Network Analysis. Группа авторов

in gathering the expected data from the website. Application Programming Interface acts as a medium of communication between the server and the client. It helps the creators to extract the data available in one location to the other with the provision of a function that assist in copying the data. The working principle of API differs from one programming language to the other. The data gathering, preprocessing, classification are the important stages in SNA, and it is depicted in Figure 1.3. Data gathering is the first step to execute any work in data mining. The process of data gathering is a flexible task, and it relies on the particular subject of user interest. Initially, the raw data are accumulated from the social network by requesting the data with a precise keyword.

After gathering the data from the social network, the data are preprocessed to execute the processes, like prediction or analysis. Based on the application, the collected data are processed with the preprocessing stages, and the data can be categorized and visualized. Nowadays, in Python, the classifiers implemented for an application is mainly any kind of the machine learning classifier that acts as a supervised machine learning approach. The classifier requires proper training using the labeled training data, without which the performance of the classifier cannot be analyzed. One of the commonly used statistical classifier is the Naïve Bayes classifier, which is generally used to classify the sentiments of people in COVID pandemic conditions. Such kind of classifiers generally utilizes the publicly available data (from the communal media data) in an efficient way to perform a prediction or analysis or classification problems.

Figure 1.3 Flowchart of social network.

1.5 Clarity Toward the Indices Employed in the Social Network Analysis

      There are a number of metrics available for the SN analysis methods that measure the activity of the social users/nodes and ensure a better understanding of the analysis [32, 33]. Some of the metrics are discussed as follows:

      1.5.1 Centrality

The evaluation of the constructional significance of a node present in an organization is executed using the metric called centrality. In other words, the preeminence of a node in an organization is deliberated using the centrality metrics. The highly influential people in the online social network can be identified using these metrics. A number of measures are used when evaluating the centrality metrics, such as Degree Centrality, Eigenvector Centrality, PageRank measure, Between-ness Centrality, Closeness Centrality, and finally, the group Centrality [34].

      1.5.2 Transitivity and Reciprocity

      The linking characteristics of a network can be accessed using the transitivity and reciprocity metrics. The transitive nature between three edges can be analyzed using the transitivity metric in such a way to develop a triangle, and in the same way, the transitive nature of a node is analyzed using the reciprocity metrics.

      1.5.3 Balance and Status

      The consistency of the networks can be evaluated using the social balance and social status metrics. The social balance theory states that a friend relationship is consistent with the propagation of the transitivity among nodes as “the friend of my friend is my friend.” Hence, the consistent triangles, depending on this strategy, are represented as balanced.

1.6 Conclusion

      SN organization examination is the way toward researching social designs using organizations and chart hypothesis. It consolidates the assortment of strategies for examining the construction of interpersonal organizations just as speculations that target clarifying the hidden elements; furthermore, designs are seen in these constructions. It is an intrinsically interdisciplinary field, which initially rose up out of the fields of social brain research, insights, and chart hypothesis.

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