# The Role of Social Network Analysis in Social Media Research
## 5 Possible Hypotheses Regarding Structural Features of Social Media Usage  
Social network analysis, as discussed in this paper, provides a means to investigate the structural features of interactions on social media. The dynamic analysis could track the changes in interaction processes and thence could make a good simulation of the real social processes. With the development of digital and internet techniques, interactions on social media could be recorded accurately, which could benefit scholars of social media research to use social network analysis to better understand the structures of interaction [45,46]. According to the literature review of social media usage and social network analysis, the concept of entropy serves as a quantification of uncertainty and disorder within a system [23,27]. It signifies that observed phenomena stem from intricately intertwined and interdependent factors, defying clear differentiation. These phenomena are characterized by  
Figure 1: Different structures of individual social networks. (**a**) Butterfly structure. (**b**) Fregmented structure (**c**) Onion structure.  
irreversibility and dynamism, rendering the recursive analysis of all contributors to human behavior unattainable. Consequently, within behavioral inquiries, the causal probabilities underlying behaviors can merely be approximated based on generalized experiences. This forms the fundamental premise of the statistical p-model method employed in social network analysis, employing the maximum likelihood estimation (MLE) technique to mitigate uncertainties inherent in observed data. In the context of MLE, one could consider the parameter estimation process as trying to reduce uncertainty in the model by finding the parameter values that make the observed data most probable. This process aligns with the idea of minimizing entropy because reducing uncertainty corresponds to increasing the probability of observing the actual data points. This method could be more reflective of the dynamic and complex systems in which the behaviors occur. Thus, the hypotheses proposed below generally use the p-model to estimate the probabilities of each structure within the observed social network, which could investigate the functional structures behind social media usage. In other words, this method could take a connective perspective to observe human behaviors, compared to the isolated and separated perspective to study human behaviors. These hypotheses brought up in this paper could be used as frameworks for future studies. More empirical outcomes should be gained in various social contexts. H1 concerns the direct interaction on social media such as likes, comments, and synchronized chat. H2 presents the passive interaction on social media such as streams of news. H3 could investigate the interaction of broadcasting on social media.  
**H1:**_The structural features of edges/2-stars/3-stars/triangles are more likely to occur regarding direct interaction on social media in the given social network._  
**H2:**_The structural features of edges/2-stars/3-stars/triangles are more likely to occur regarding passive interaction on social media in the given social network._  
**H3:**_The structural features of edges/2-stars/3-stars/triangles are more likely to occur regarding broadcasting interaction on social media in the given social network._  
More than these three hypotheses may be assumed in practical applications about the probability distributions of various social network structural features. The specific hypotheses should be based on scholars' research objectives. The more precise the information gathered from social networks on social media, the more precisely the probability distributions could be modified. Through empirical investigations, the levels of interactions from the micro level of people to the macro level of social groups and other social entities can also be included.