There are several Online Research Methods (MIO), which allow researchers both inside and outside the academic field, access to tools and above all, possibilities that expand, magnify and deepen the options that exist in the non-digital world. The new possibilities are not difficult to be adopted by those who are used to the old forms of research and they use artificial intelligence to accelerate and optimize processes that previously could take longer. Social media analysis is one of these areas.

In this regard, social scientists have developed techniques of analysis and visualization of social networks for decades. Social network analysis provides a set of powerful quantitative graphical metrics for understanding networks and the individuals and groups within them. This technique was helpful in identifying prominent characters in the Les Misérables matching network. The combination of metrics and visualization provides a powerful means of understanding and exploring the relationships between its components.

What is Social Network Analysis?

The Analysis of Social Networks (ARS), according to Herrero (2000) is a process of investigation of social structures that uses the Theory of Networks and the Theory of Graphs. It considers network structures in terms of nodes (individual actors, people or things inherent in the network) and the links, edges or connections - be they relationships or interactions - that connect them. Examples of social structures commonly analyzed through the ARS include:

  • Social Media Networks
  • The viralization of Memes
  • Circulation of Information
  • Networks of friends and acquaintances
  • Business Networks
  • Knowledge network
  • Social relationships
  • Collaboration graphs
  • Relationship
  • Transmission of diseases
  • Sexual intercourse

In the analysis of social networks, the analysis of the micro-macro link is a very important element. In the same way, it is the way in which individual behavior and social phenomena are connected to each other. In this perspective, social media is both the cause and the result of individual behavior. Social media provides and limits individual choice opportunities. While at the same time people initiate, build, maintain and break relationships and in doing so determine the overall structure of the network. However, the network structure as it exists is seldom consciously constructed by its individuals. It is often the unintended effect of individual actions and as such can be called a spontaneous order.

Origins of Social Network Analysis

This new discipline at MIA has its roots in the work of pioneering sociologists such as Georg Simmel and Emile Durkheim. These authors wrote about the importance of studying the patterns of relationships that connect social actors. Social scientists have used the concept of “Social Networks” since the early 20th century to identify groups of complex relationships between members of social systems on all scales, from the interpersonal to the international.

In the 1930s, basic analytical methods were introduced by Jacob Moreno, founder of Psychodrama and Group Psychotherapy. Especially Helen Jennings, pioneer in this field. Quantitative research methods were developed that led to the creation of Sociometry, a method of measuring social relationships. His work is what gives rise to the Analysis of Social Networks. John Arundel Barnes began to use the term systematically to indicate tie patterns. Concepts traditionally used by the public and by the academy were harmonized, namely: united groups (tribes, families) and social categories (gender, ethnicity).

How Social Network Analysis works

Social network analysis considers inter-organizational networks as a set of links (for example, resources, friendship, informational links) between a set of actors (individuals, groups or organizations). Social network analysis considers inter-organizational networks as a set of links (for example, resources, friendship, informational links) between a set of actors (individuals, groups or organizations).

The basic objective is to understand how the general structure of an inter-organizational network, and the position of an actor within that network, provides opportunities and limitations for the behavior of the actors. Compared to research on inter-organizational relationships, it pays more attention to the overall integration of organizational and individual actors. In the same way, it takes into account the networks of relationships and the individual attributes of the actors and their individual relationships.

Métodos principales de análisis de Redes Sociales

The simulation and the experiment represent important methods within the analysis of social networks. However, more and more analysts have begun to complement these traditional methods with survey research and ethnographic analysis. Important concepts that have been explored include network and actor centrality. That is, measures of the connection of the actors within a network. Measures relating to the structural properties of networks, such as the density of a network, are also included. This indicates the number of links established in relation to the possible links between the actors of a network.

In the same way, the concept of a clique is used, which denotes a subset of actors within a network, which are directly linked to each other. By last, according to Adler Lomnitz (1994). measures of similarity can also be used. For example, structural equivalence as two actors occupy structurally equivalent positions if they maintain identical links with the same third parties in the network.

Use of Mathematical Tools

Social network analysis uses mathematical tools to systematically understand networks. They are formed by vertices or people who are connected to each other through edges, for example, friendship ties. Las métricas de red ayudan a identificar quién es más importante o central en una red. Similarly, they can also be used in subgroups of closely connected people and in the general structure of the network.

In this way, the metric used includes:

  • Connections: Homophilia, Closure of networks,
  • Distributions: Bridge, Centrality, Distance, Density
  • Segmentation: Cohesion, Grouping Coefficient, Density

The analysis of social networks suggests two general ways in which the general structural characteristics of social networks influence the behavior of the actors. First, the particular structures and positions of the network have implications for the information that is available to the actors. Therefore, they configure their decision making and their behavior.

Second, network structures and particular positions in various ways invest actors or deprive them of power to exercise control over other actors. Such power may come, for example, from the use of informational advantages. Similarly, it may be a consequence of privileged access to other actors, the invocation of obligations associated with private relationships or the possibility of mobilizing sanctions by third parties.

Sociograms

Social networks are commonly visualized through sociograms. In the sociograms the nodes are represented with points and the links are represented with lines. This way of visualizing makes it possible to qualitatively evaluate networks, since they vary the visual representation of their nodes and edges, in order to reflect attributes of interest.

ARS has emerged as a key technique in modern Sociology that includes political campaigns and the prediction of events such as Pandemics and Epidemics. It has also been shown to be key in research in Anthropology, Biology, Demography and Social Communication Studies. It has been particularly important in Economics, Geography, History, Information Sciences, Organizational Studies and Political Sciences. Public Health, Social Psychology, Development Studies, Sociolinguistics, and Computer Science have benefited widely. It has also got applications in the fight against money laundering and against terrorism.

Social capital

The notion of social capital stems from community studies that highlighted the role of strong and intense personal relationships. This occurs among members of a local community that invests them in mutual trust and power for collective action. Social capital grants actors a credential that entitles them and a resource that can be used in action. At least two competing conceptualizations of social capital can be distinguished.

A structural vision sees social capital as an information and control advantage. This is enjoyed by the actor who is in an intermediary position between actors who would not otherwise be connected. This position exploits a "structural hole" in a network.

A relational view sees social capital as a function of the intensity and extent of personal ties between actors. In this perspective the strong ties between the actors are considered. Similarly, intense relationships characterized by mutual trust, a sense of obligation, common norms and expectations are taken into consideration. This creates a social capital in which the respective actors can have recourse.

Consequences of Social Capital

When examining the consequences of social capital, two main effects have been identified. First, equity capital can be used to increase the efficiency of the action by increasing the effectiveness of the dissemination of information. Strong ties and intense relationships between actors who share many characteristics make information exchange easier and less ambiguous. Strong ties and intense relationships between actors who share many characteristics make information exchange easier and less ambiguous. This minimizes redundant contacts.

Second, according to White (2000), social capital can be used as a productive resource that improves the achievement of the actors' objectives. This can be accomplished, for example, by exploiting the position of a structural hole to learn and exercise control over more rewarding opportunities. For example, regarding career advancement or business benefit. Likewise, in densely connected networks characterized by strong ties between actors, information on the behavior of the actors is quickly disseminated and evaluated based on similar standards. Therefore, behavior can be easily socially sanctioned, cooperation is enabled, and opportunism is limited. Thus, inter-organizational networks whose members possess significant relational social capital were found to thrive and prosper.

Conclusions

Social network analysis is the study of the patterns of social relationships that comprise social structures. Treating these relationships as networks of connections between the individuals and groups that enter them. While these relationships may be made up of particular individuals, social network analysis is not limited to interactions at the micro level. Individuals form social relationships as the occupants of institutionally defined positions in social organizations. And social relations enter into the constitution of relations patterns at the macro level that can also be treated as social networks.

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A large number of concepts have been developed to characterize, measure, and compare network structures and positions in networks. These include the relative centrality of individuals, groups, and positions within networks; its grouping into subgroups; the cohesion or general density of a network; and the centralization of networks around focal points. Various statistical methods have been developed to estimate their values ​​and evaluate the importance of these measurements for the observed results.

Bibliographic references

Adler-Lomnitz, L. (1994). Redes sociales, cultura y poder. Ensayos de antropología latinoamericana. México: Miguel Angel Porrúa.

Herrero, R. (2000). “La terminología del análisis de redes. Problemas de definición y de traducción” Política y sociedad, (33) 199-206.

White, H. (2000), “La construcción de las organizaciones sociales como redes múltiples.” Política y sociedad, (33) 97-104.

 

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