Social network analysis (SNA) is a research method that examines social structures through the use of network and graph theories (Borgatti et al., 2009; Knoke & Yang, 2008; Scott, 2013). The origins of SNA can be traced back to the early 20th century, when sociologists began to study the relationships between individuals and groups. However, it wasn’t until the 1960s that SNA gained popularity as a distinct research method.
The first major study to use SNA was conducted by social psychologist Stanley Milgram, who famously demonstrated the “six degrees of separation” phenomenon (Wasserman & Faust, 1994). Milgram’s research involved sending letters to individuals in the United States, asking them to forward the letters to someone they knew who they thought would be closer to a target person in Boston. Through this experiment, Milgram found that, on average, it took only six intermediaries to connect any two individuals in the United States.
Since Milgram’s study, SNA has been used in a variety of fields, including sociology, psychology, anthropology, and more recently, marketing and business.
One of the pioneers of social network analysis was J.L. Moreno, a psychiatrist and sociologist who developed the concept of the sociogram in the early 20th century (Moreno, 1934). Moreno used the sociogram as a tool to visually represent the relationships between members of a group or community.
The sociogram is a graphical representation of a social network, where nodes represent individuals or groups, and links represent relationships between them. The sociogram can be used to identify key individuals or sub-groups within a network, and to analyze the flow of information and influence within the network.
Social media platforms like Facebook, Twitter, and LinkedIn have become a rich source of data for social network analysis. These platforms allow users to create and maintain social connections with other users, making it possible to map and analyze social networks.
Through the analysis of social media data, researchers and marketers can gain insights into the structure and dynamics of social networks, including who the key players are, how information flows through the network, and how communities form and evolve over time.
Social network analysis has many applications in digital marketing. For example, it can be used for influencer marketing, community management, and content marketing. Social network analysis can help identify influential users and opinion leaders within a network, the most active members of a community, and the topics and content that resonate most with a target audience. This information can be used to develop more effective marketing strategies that leverage the power of social networks.
One of the leading scholars in the field of social network analysis is Ronald Burt, a sociologist at the University of Chicago. Burt has made significant contributions to the study of brokerage and structural holes in social networks, and his work has been influential in fields such as organizational behavior and entrepreneurship (Burt, 2005).
Another prominent scholar in the field is Mark Granovetter, a sociologist at Stanford University. Granovetter is known for his work on the strength of weak ties in social networks, which has had a significant impact on the study of social networks in a variety of fields, including sociology, economics, and political science (Granovetter, 1973).
Finally, Kathleen M. Carley, a computer scientist and sociologist at Carnegie Mellon University, is known for her work on dynamic network analysis and computational social science. Carley’s research has focused on understanding how social networks evolve over time, and how they can be used to detect and respond to threats such as cyber attacks and terrorist networks (Carley, 2002).
These scholars, along with many others, have made important contributions to the field of social network analysis, and their work continues to shape our understanding of the structure and dynamics of social networks. In conclusion, social network analysis is a powerful research method that can provide valuable insights into the structure and dynamics of social networks. With the increasing importance of social media in today’s digital landscape, social network analysis has become an essential tool for marketers looking to understand and engage with their target audiences. Moreno’s work on the sociogram laid the foundation for social network analysis as we know it today, and the contributions of contemporary scholars continue to push the boundaries of the field.
Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892-895.
Burt, R. S. (2005). Brokerage and closure: An introduction to social capital. Oxford University Press.
Carley, K. M. (2002). Dynamic network analysis. Handbook of computational sociology, 717-732.
Granovetter, M. (1973). The strength of weak ties. American journal ofsociology, 78(6), 1360-1380.
Knoke, D., & Yang, S. (2008). Social network analysis (Vol. 154). Sage Publications.
Moreno, J. L. (1934). Who shall survive? A new approach to the problem of human interrelations. Beacon House.
Scott, J. (2013). Social network analysis. Sage Publications.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press.