Throughout history, well-known scholars such as Auguste Comte and Émile Durkheim talked about how social actors are connected to each other and how a society can be understood by simply looking at the way its members are connected. Social networks, which are the maps of interconnections among socially related people, clearly play important roles building social structures.
Human beings, sometimes called social animals, live in social networks; from criminal activities to the spread of innovations, almost every social phenomenon can be explained by social networks. Mainly driven by the personal needs for communication and belongingness and influenced by gender, income, education, personality, and attractiveness, the size of a social network in real life is expected to be about 125–150 people.
However, only four of these contacts are considered a real source of help during severe hardships. Although we are genetically programmed to build and maintain a social network, it is suggested that new communication technologies such as the telephone and the Internet dramatically impacted our social networking behavior.
(Taken from Acar, A. (2008). Antecedents and consequences of online social networking behavior.)
Impact of Social Media
The impact of social networks on our lives may be larger than we realize. Fowler and Christakis, the authors of the book Connected, found that our happiness, our weight, our health, and even our life expectancy can all be predicted by our social network or the people to whom we are either directly or indirectly related.
A longitudinal study that monitored the weight of people in a small town found that obesity spread just like a virus among network members: if people started living with a fat person or interacted with people who were fat eventually they grew fat themselves. Similarly, the authors indicate that people who have a happy friend in their network are significantly more likely to be happy and those with a lonely friend are 50% more likely to feel lonely.
The book also discusses how men who were surrounded by more men during their adolescence were likely to have a shorter life and how Harvard students were significantly more likely to get a flu shot if they had a friend in their social network who got one.
Times are changing and social networks are growing ever more important. Because of digital convergence —a term that refers to the integration of all new media organs (TV, Internet, newspapers, etc.)—as well as the interactivity of the media and the storage and forwarding capacity of new communication channels, governments, cities, and people are now more networked. This phenomenon is also known as the network society.
According to Van Dijk, new media enables each person to discover social information and, more importantly, eliminates the need for face-to-face communication. When we don’t depend on face-to-face communication, time and space limitations of human communication become irrelevant, meaning barriers for network communication and information flow among network members disappear.
Therefore, modern social networks, driven by people who can skillfully use the Internet and advanced communication technologies, are more effective and have more influence on societies in the 21st century.
Social Network Definition
A social network is defined as “a set of people (or organizations or other social entities) connected by a set of social relationships, such as friendship, co-working or information exchange.” A broader definition would be, “a social structure made up of individuals (or organizations) called ‘nodes,’ which are tied (connected) by one or more specific types of interdependency, such as friendship, kinship, common interest, financial exchange, dislike, sexual relationships, or relationships of beliefs, knowledge or prestige.”
Each node represents a member of a social network. Connections between the nodes are called ties. If two nodes are strongly related or frequently contact with each other the tie between them is considered to be “strong” whereas loose connections are usually referred as “weak” ties.

Rules of Networks & Social Networks
Growth: Our network always grows because the number of people we meet is higher than the number of people from whom we disconnect. The fact that the world population, the Internet, and the number of communication channels are also growing indicates the speed of growth in our social networks may accelerate.
Preferential attachment: New members of any social network prefer to connect to those with a larger number of connections because this would maximize their chances of reaching the highest level of information in the shortest time.
Homophily: Our friends tend to be similar to us. In other words, we tend to connect and communicate with those who resemble us (the same gender, age, school, town, culture, etc.).
Reciprocity: Just like human relationships in real life, social network relationships are reciprocal. If someone sends us a message, we tend to reciprocate. If someone follows us on Twitter, we tend to follow that person as well. If someone leaves a comment on our picture, we tend to do the same.
Transitivity: Our friends often know each other. We usually connect with people who also know each other, and we usually have only a small number of connections who don’t know anything about our friends. This helps us get more support from our social networks and spread the information more quickly.
The power of betweenness: This is also known as betweenness centrality, which refers to the power a node has based on how easily that node can connect to all other nodes in the network. In other words, the most influential person in a social network is not the member with the largest number of direct connections, but the one who directly or indirectly knows the most network members. Put differently, the more people depend on a network member to distribute information, the more power he or she has. For instance, the principal of our school might not have many direct connections in the school itself, but if we want to send information to a teacher in another school we may rely on him. When a network size increases, the power of the person in the center also increases.
Metcalfe’s Law: The value, the productivity, and the power of a social network always increase exponentially when new members join. In any given network, if we presume that all members are connected to each other, there would be a total of n × (n – 1) / 2 connections. If there are five people in a network, the number of total connections would be ten, i.e., 5 × (4 / 2). If the number of network members increases by one, then the number of total connections increases by 50% to fifteen, i.e., 6 × (5 / 2).
Value = n × (n – 1) / 2
Size and density: Small networks tend to be denser, where everybody knows everybody; and large networks tend to be sparse, where we are only directly connected to a small portion of the network. Density is usually calculated as the number of total direct connections divided by total possible connections. A network’s density is considered 1 if everybody is connected to each other.
The law of diminishing influence: A member’s impact on the network dissipates node by node. We have some influence on our first-degree connections, little influence on the second-degree connections, and very little influence on the third-degree connections. We may have influence on our friends, some indirect influence on friends of friends, a trivial influence on friends of friends of friends, and no influence at all on friends of friends of friends of friends.
Granovetter effect (the strength of weak ties): When it comes to getting help in real life, weak ties are more beneficial for us. Granovetter claimed that our connections whom we barely know are likely to be connected to those we cannot reach. Strong ties are not as useful because they are likely to know people we already know, meaning they cannot introduce us to a new opportunity.
Dunbar’s number: Robin Dunbar measured the size of groups formed by different primates and concluded that the cortex size (the frontal area of the brain) determines the group size animals form. Animals with bigger brains can form larger groups, because a larger cortex empowers them to remember other members of their pack and successfully communicate with the rest of the herd when predators attack or when hunting for food. Dunbar also analyzed human social networks by assessing Christmas cards people send to their friends and family and found that in Western societies the average size of a social network is about 150 people. Our brains cannot successfully handle more than 150 people, according to Dunbar.
Bacon number: The Bacon number refers to the degree of separation of each actor and actress in Hollywood from Kevin Bacon. Since Kevin Bacon played in various movies with many different actors, it was observed that he could be connected to any actor or actress with only a few nodes. For instance, Japanese actress Rinko Kikuchi’s Bacon number is only 2, because she starred with Jamie McBride in the movie Babel (2006). McBride, in turn, played with Kevin Bacon in Beauty Shop (2005), thus making Kikuchi only two nodes away from Kevin Bacon (one node is a direct connection).
Six degrees of separation: In the 1960s the psychologist Paul Milgram theorized that any two randomly selected people in the United States can be connected to each other by their friends, via friends of friends of friends of friends of friends. To prove his theory, he asked people in Omaha, Nebraska, to deliver a package to a stockbroker in Massachusetts that no one in Omaha knew about. By giving the package to someone who lived close to Massachusetts and having that person give the package to someone who may know that stockbroker, the participants of the experiment eventually managed to get the package delivered. Milgram found that on average the package was exchanged among six different people. He concluded that any two randomly selected people in the USA are actually only separated by six degrees.
Winner-takes-all: Networks and connected platforms are usually dominated by one central node. This is related to betweenness centrality; the bigger a social network becomes, the less likely two central nodes can have equal power.20 Many services that are dependent on other people’s membership are dominated by one big player: Facebook dominates online social networks; Google dominates search engines; English dominates languages spoken all around the world; Microsoft Windows dominates operation systems; eBay dominates auction sites. People want to be connected by using the shortest path, and having one big central node makes these connections easier, faster, and more convenient.
Groups & Social Networks
The terms social networks and groups may be used interchangeably, but there is a major difference between them: a group is a circle of people who have come together for a common goal, while a network is the map of ties displaying how those members are connected. The most important elements of groups are interaction and intergroup communication, as explained below:
To count as a group, a social entity must have regular member interaction. Most commonly, this means either speaking, signing, or typing to one another, though some groups’ most important interactions are physical or nonverbal, as in the case of a play group, jazz band, or work crew. If communication does not occur with any regularity in a group, there may exist a social gathering or relationship network of some kind, but not a group. After all, the very idea of grouping entails an ongoing pattern of communication among the group’s members.
(The Group in Society, p. 7)
We naturally join groups because of our needs of survival, social belonging, and social reference. Studies show that groups in general make better decisions than individuals.
Perhaps because of this we are usually more influenced by people with whom we share a group. We usually gain a sense of cohesion, belonging, and social identity by being in groups.
On the other hand, we tend to experience disinhibition (we can’t control what we do), deindividuation (we conform to group norms), and polarization (our decisions, influenced by group dynamics, fall into more extreme ends) as a result of being in a group.
Some scholars claim that group influence on individuals is very strong and individuals usually misjudge the risks of a decision (groupthink) and easily change their attitudes and/or behaviors based on how the other members of their group are acting (social conformity).

According to Paul Adams, we usually belong to between four and six groups based on
1) our life stage (relatives, in-laws)
2) shared experiences (college, work, trip); and
3) shared interests (hobbies, circles, etc.).
When our life stages, careers, and hobbies change, the groups to which we belong change as well. For instance, during early childhood we would have a playgroup from our street, and then we would have groups in our elementary school or a fraternity at college.
We may later on join and leave a number of interest groups, including politically affiliated groups, hobby groups, and neighborhood-watch groups. Although the people we interact with change throughout our lives, there is always the rule of 5-15- 50-150-500.
We all have an inner circle (5 people), a sympathy group (15 people), occasional contacts (50 people), a maximum group (150 people), and weak ties (acquaintances, friends of friends, etc.)
Community, Society & Social Networks
The proponents of social capital theory claim that people have a natural tendency to interact with other members of society and build functioning networks that usually result in physical, informational, financial, or other forms of gains for all members. However, not all ties in social groups can be considered the same in nature.
More than one hundred years ago, the German social scientist Tönnies proposed that social relationships can actually be classified into two types: community (gemeinschaft) and society/association (gesellschaft). Usually community relationships tend to be affective; members of a community are likely to share the same place and similar values.
An example of a community would be family, a network of relatives, or a small village where people are closely related. Associations/societies, on the other hand, bring members together around a shared goal, such as a group of employees working in a factory.
Their relationships are rule-based, and what keeps them together is their desire to achieve their own goals. We should note that cyberspace relationships tend to be of the gesellschaft type, as they don’t always depend on shared location or shared beliefs.
Social scientists also believe that the way people form networks in society may depend on “social distance,” which can be affective, normative, and interactive.
(1) Affective social distance is feeling closer to or distant from certain people based on individual affection.
(2) Normative social distance is related to the social norm of including all members of our social group into our network, while excluding all non-members of our social group from it.
(3) Lastly, as its name indicates, interactive social distance refers to reduced social distance as a result of frequent or important interactions.
Online Social Networks
The history of online social networks goes back to 1978, when computer scientists Murray Turoff and S. Roxanne Hiltz established the Electronic Information Exchange System at the New Jersey Institute of Technology for the U.S. Office of Civilian Defense. The system allowed users to email each other, see the bulletin board, and utilize the list server.
About twenty years later, in 1997, sixdegrees.com (the name refers to Milgram’s famous small-world study revealing that two randomly selected American citizens can be connected to each other by six nodes) became the first widely known website to allow its users to establish an online social network. This was followed by the online business network of Ryze.com (2001) and then Friendster.com (2003), an online social networking service that enjoyed popularity all around the world.
The following timeline28 nicely illustrates the development of online social networks in the Western world:

A simple definition of online social networks is, “web-based services that allow individuals to
(1) construct a public or semi-public profile within a bounded system,
(2) articulate a list of other users with whom they share a connection, and
(3) view and traverse their list of connections and those made by others within the system” (p. 211).
Today, with the inclusion of Google+, Tumblr, and Pinterest, it becomes increasingly hard to define social network sites. For instance, one may refer to Pinterest as an interactive curating site, even though it meets all three criteria outlined in the definition above.
Perhaps because of this difficulty, the term online social networks (OSNs) seems that it is being replaced by the term social media. A quick check on Google Scholar shows that there are currently more than 9,000 academic papers with a title that includes the words “social media.”
Strangely, in 2005, when both social media and OSNs were new, online social networks was used more often. This indicates that the terms are very similar, though recently social media is preferred.

Social networks are investigated by different scholars from various disciplines and there is no unifying theory that can explain how social networks operate. Additionally, most social network research was conducted before social media became mainstream, meaning the findings were influenced by “time and space” limitations of social network communications.
In today’s online social networks, some of the rules mentioned above may not hold true. For instance, despite the fact that we have a notion of six degrees of separation, on Facebook or Twitter any two people from all around the world can be connected to each other in fewer than four degrees (this is because celebrities may have social connections from all around the world).
In the same vein, people usually have more than 150 friends on Facebook, which contradicts the norm of the Dunbar number. Furthermore, information flow between two members of a network usually follows the shortest available path available.
For instance, if someone in India wants to call a person from Japan, it would be through a hub that is geographically closest to Japan (e.g., China or Korea). However, nowadays most information flow in telecommunications is through the United States, which may represent the cheapest path but not the path of minimum distance between the two nodes (that is, geodesic).
Lastly, because of new social media technologies, we may influence people who are more than three degrees away. We all may have shared messages in social media that were posted neither by our friends, nor our friends’ friends.
References
- Kadushin, C. (2005). JoSS Book Review of Linton C. Freeman, The Development of Social Network Analysis: A Study in the Sociology of Science. Journal of Social Structure, 6 (1).
- Degenne, A. & Forse, M. (1999). Introducing Social Networks. Thousand Oaks, CA: Sage Publications.
- Kiehne, T. P. (2004). Social Networking Systems: History, Critique, and Knowledge Management Potentials. Unpublished manuscript. Austin, TX: University of Texas.
- Hill, R. A. and Dunbar, R. I. M. (2003). Social network size in humans. Human Nature, 14 (1), 53–72.
- Kiesler, S., Kraut, R., Cummings, J., Boneva, B.; Helgeson, V. and Crawford, A. (2002). Internet evolution and social impact. IT & Society, 1(1), 120-134
- Christakis, N. A., & Fowler, J. H. (2009). Connected: The surprising power of our social networks and how they shape our lives. New York: Hachette Digital, Inc.
- Yoffie, D. B. (Ed.). (1997). Competing in the age of digital convergence. Harvard Business Press.
- Van Dijk, J. (2012). The network society. Sage Publications.
- Garton, L., Haythornthwaite, C. & Wellman, B. (1997). Studying online social networks. Journal of Computer-Mediated Communication 3 (1), 0-0 (Online resource).
- Shekhar, S., & Oliver, D. (2010). Computational modeling of spatiotemporal social networks: A time-aggregated graph approach. In Specialist Meeting-Spatio-Temporal Constraints on Social Networks.
- Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286 (5439), 509-512.
- Apicella, C. L., Marlowe, F. W., Fowler, J. H., & Christakis, N. A. (2012). Social networks and cooperation in hunter-gatherers. Nature, 481(7382), 497-501.
- Newman, M. (2009). Networks: an introduction. Oxford University Press.
- Metcalfe, B. (1995). Metcalfe’s law: A network becomes more valuable as it reaches more users. Infoworld, 17 (40), 53-54.
- Adams, P. (2011). Grouped: How small groups of friends are the key to influence on the social web. New Riders.
- Granovetter, M. (1983). The strength of weak ties: A network theory revisited.Sociological theory, 1(1), 201-233.
- Dunbar, R. I. (1993). Coevolution of neocortical size, group size and language in humans. Behavioral and brain sciences, 16(4), 681-693.
- http://oracleofbacon.org/
- J. Travers and S. Milgram. An experimental study of the small world problem. Sociometry, 32(4), 1969.
- Freeman, Linton (1977). “A set of measures of centrality based on betweenness”. Sociometry 40: 35–41
- Gastil, J. (Ed.). (2009). The group in society. Thousand Oaks, CA: Sage.
- Janis, I. L. (1983). Groupthink (pp. 2-13). Boston: Houghton Mifflin.
- Lin, N. (1999). Building a network theory of social capital. Connections, 22(1), 28-51. 24. Tönnies, F. (2012). Gemeinschaft und gesellschaft (Gemeinschaft and gesellschaft). Books on Demand.
- Malberger, O. M., Rafaeli, S., Waisman, D., Bader, D., & Drugan, A. (2013). Media selection for the announcement of childbirth-The social capital aspect. In The Third International Conference on Digital Information and Communication Technology and its Applications (DICTAP2013)(pp. 12-23). The Society of Digital Information and Wireless Communication.
- Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge, UK: Cambridge University Press.
- Freierman, S. (1998, June 4). Screen grab; 6 degrees of networking, New York Times, Thursday, Late Edition – Final, p. G10
- Boyd, D. (2004). Friendster and publicly articulated social networks. In CHI 2004. Conference on Human Factors and Computing Systems, April 24-29. Vienna: ACM Press, 1279-1282.
- Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer;Mediated Communication, 13(1), 210-230.