# The Role of Social Network Analysis in Social Media Research
## 6 Conclusions  
Social network analysis has been introduced for the investigation of social media usage in this review paper. Human interactions are dynamic and complicated social processes; hence an appropriate approach for dynamic processes is needed to describe these variations [24; 31]. Previous theories have frequently used institutionalized norms and values--necessary but insufficient--to describe interactions. However, stochastic and urgent events like strikes, rumor diffusions, and riots frequently occur in real life, and human interactions don't always adhere to general values and norms. People with the same values and norms might be compelled by these events to behave differently than their usual behavioral patterns. Therefore, instead of making assumptions about a straightforward relationship between collective results and individual values and norms, employing a dynamic and systematic perspective to study interactions is preferable [3; 10; 20].  
Social media has given us the perfect platform to observe dynamic interactions, at both the micro and macro levels [14; 21; 25]. Social media has made it possible to research digital records of interactions by using interactions as analytical units and data, which allows social network analysis to use mathematical techniques to analyze dynamic interactions on social media. Structural features are crucial to behavioral studies because they are functional to behaviors. Different structures could help or hinder the performance of behaviors.  
In the past, dominant theories have tended to use well-established and agreed-upon patterns to describe social behaviors, while structural features that have strong interpretive capabilities for stochastic and dynamic interactions have been mostly overlooked. The underdevelopment of systems for tracking and analyzing interactions on various scales was a major contributor to the neglect. The potential of social network analysis to analyze dynamic interactions, however, would be more and more apparent with the development of the internet and digitalization techniques, which requires additional empirical research to verify it.  
The adoption of relational and interactive social network analysis perspectives may transform the research paradigm of social media studies. Social network analysis could replace isolated and static perspectives in studying interactive behaviors. Social network analysis uses a variety of mathematical techniques, such as maximum likelihood estimation and p-models, for studying stochastic and dynamic events to investigate the structural features of social media usage, which could be reflective of real contexts with certain levels of chaos, or entropy. Future works using social network analysis could use these techniques and corresponding hypotheses to investigate social media usage with a systematic and relative perspective, even in cooperation with other disciplines to address the complexity caused by entropy in systems. These techniques have the potential to profoundly reveal the underlying mechanism behind social media usage, so it is worthwhile for more social media studies scholars to pay more attention in the future.  
Conceptualization, Z.N.; methodology, Z.N.; writing--original draft preparation, Z.N.; writing--review and editing, M.W.; supervision, M.W., D.K. and W.A.B.W.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.