# The Role of Social Network Analysis in Social Media Research
## 4 The Related Works of Social Network Analysis  
The research methodology for social media usage should be able to capture the dynamic interactions between social media usage and social contexts since human behaviors, including the use of social media, are susceptible to complex social situations [21; 22]. Social network analysis may be one of the approaches that are best suited to capture these interactions. According to its definition, social network analysis (SNA) is the process of analyzing interaction structures by designating interacting objects, such as people or other entities, as nodes and the relationships between them as lines or edges [3; 5]. Using this technique, network graphs made up of nodes and lines can be drawn, and the structures can then be institutionally presented. The interaction between the micro level, which includes individuals, and the macro level, which includes various social groups, could be captured through social network analysis since connections on both levels are made into visible graphs. It is a research method that investigates both the micro and macro levels of human behavior, making it potentially more reflective of actual human behavior. Because most human behaviors always take place among interactions between micro and macro levels, humans learn about the outside world and adjust their behaviors through interaction and exchange with the outside environment; therefore, understanding the interactions between individuals and their environments is crucial [3; 5; 20]. Although a little abstract, it is clear that if individuals act independently and without any interactions, they may be free to act however they like, making their behavioral patterns elusive and unpredictable. However, when acting in social groups, an individual's behavior is governed by the disciplines present in these groups because the freedom an individual has is constrained by groups. These laws resemble the entropy laws of physics [23]. Entropy is a fundamental concept utilized to gauge the level of uncertainty or randomness pertaining to the outcomes of a random variable [23; 24; 25; 27]. It provides a quantitative measure of the average information necessary to describe the various possible states or forms exhibited by a factor. Essentially, the number of potential manifestations of a random factor X is contingent upon the entropy value of the system in which it operates. The explicit formula representing entropy is presented below:  
\[\text{H(x)}=-\Sigma\text{P(x)}\times\log_{2}\text{P(x)} \tag{1}\]  
Formula (1) introduces the concept of entropy [24], where H(x) denotes the entropy of a random variable X. P(x) represents the probability of a specific outcome or event x, emphasizing the likelihood of observing that particular outcome. The symbol \(\Sigma\) denotes the summation operation, requiring the subsequent expression to be summed over all possible outcomes x of the random variable X. The logarithm base 2, denoted as log\({}_{2}\) P(x), scales the probabilities logarithmically and serves as a measure of information. By multiplying P(x) by log\({}_{2}\) P(x), we obtain the information content associated with each outcome, quantifying the number of bits needed to transmit the outcome x. Furthermore, the negation of the sum of these values (\(-\Sigma\) P(x) * log\({}_{2}\) P(x)) ensures that the entropy is expressed as a positive value, effectively capturing the average amount of information required to describe the random variable X. The basic logic of entropy is widely employed in statistical analysis [28; 29; 30]. Probability serves as a pivotal concept that captures the interplay among factors within a specific system, acting as a bridge between micro-level and macro-level phenomena. Researchers have observed that isolated individual particles demonstrate stochastic and unpredictable behaviors. However, when multiple particles interact, their collective behaviors manifest discernible and recurring patterns, enabling estimation of the probabilities associated with each pattern.