Social networks are complex entities. In fact, social network analysis has evolved as a mathematical tool to model the intricate workings of social networks. Mathematical algorithms that map out social networks include the social network potential (SNP), a mathematical coefficient that represents both the size of an individual's social network and their ability to influence that network. Many variables contribute to a person’s SNP, including group memberships, leadership roles, peer recognition, publication of electronic and non-electronic media, and frequency of past distribution of information within a network.
Although social network analysis is an effective tool for mapping out the complexities of social information exchange, this inherent complexity is also its shortcoming. Social network analysis focuses on the network itself, not on the individual. What appears to be lacking is a simple metric that measures the magnitude of information a person exchanges within a social network regardless of the type of information exchanged, the complexity of the network, or the level of influence the person has on the network. I call this measure the Social Network Information Value (SNIV). It is defined as: the magnitude of information a person exchanges within a social network. This magnitude is defined by two factors: 1) the overall size of the network in terms of direct contacts to other people (via an electronic or physical medium), and 2) the amount of personal information (P) that the person exchanges with each member of their network. If one considers that each person in a social network has a certain amount of personal information that can be shared, the maximum value of P=1 (or 100%) only happens if a person shares EVERY bit of personal information, including demographic information, private medical information, personal thoughts, secrets, etc.
A visual model of SNIV is shown below. Person A and Person B each have multiple direct contacts (smiley faces). Imagine that the size of each smiley face represents the amount of information exchanged. Person A exchanges more information with her close friends than does Person B, but Person B has more direct contacts overall. Therefore, Person B may have a greater SNIV depending on the exact number of contacts and the amount of information exchanged with each contact.
Mathematically, this can be expressed as:
Where n equals the total number of personal contacts in the social network for a given person, Pi is the percentage of personal information exchanged with each person i, and P bar equals the average amount of personal information exchanged.
In reality, this model is slightly more complex. P, the percentage of information that one exchanges with a social contact, is really a combination of the personal information that one gives out (shares) and the personal information that one receives (cares). People within a social network do not necessarily give and receive information equally. Some are more likely to relate information about their personal life, but do not wish to hear about the lives of their contacts. Others may thrive on gossip and scuttlebutt, but they are very protective of their own privacy. By plotting a person’s overall SNIV on a y-axis and a person’s ratio of sharing to caring on an x-axis as shown below, one can visualize four emerging population segments: Hermits, Socialites, Voyeurs, and Exhibitionists.
Hermits have very few to no personal contacts, and they exchange little to no information. Socialites have many social contacts, and they maintain a balance between information given and received. Voyeurs and Exhibitionists are obviously the extremes of the sharing/caring balance described above.
Therefore, if one assumes that P is in fact the sum of the share factor (percentage of personal information one shares with each contact, S) and the care factor (percentage of personal information one receives from each contact, C), the SNIV equation is rewritten thusly:
In other words, a person’s SNIV consists of the total amount of information one gives out to one’s contacts plus the total amount of information that one receives from one’s contacts. To maximize one's SNIV, one needs to balance sharing and caring.
More importantly, a related value can be calculated to ascertain a person’s potential for engaging in a social network. I call this value the Social Network Information Threshold (SNIT). The SNIT can be defined as the total amount of information one is willing to give out to one’s contacts plus the total amount one is willing to receive. The quotient of SNIV over SNIT (QSNIT) reflects the balance of one’s potential for sharing information compared to one’s actual practice of sharing information.
The psychological implications of this balance could be a whole field of study in itself. A person with a QSNIT of 1 is in equal balance. Whether this person prefers to be a Hermit, Socialite, Voyeur, or Exhibitionist, this person is exactly as connected as he or she wishes to be.
However, a QSNIT below 1 suggests that the person is not as socially connected as he or she would like, and this could lead to feelings of alienation and depression. A person operating below threshold must find a way to reconnect with a social network to maintain balance. Simply establishing personal connections is not enough to raise one’s QSNIT. One must actually exchange personal information to raise the quotient.
Social network sites are effective ways of raising a QSNIT in that they give a person an almost limitless number of contacts and an easy way to exchange information. What has to be managed, however, is exactly how much information is given or received. The ease that one can disseminate personal information to others through a social network site can artificially raise a person’s QSNIT above 1. When this happens, a person moves past their privacy comfort level. What is most insidious about social network sites is that a person may not realize they have exceeded their privacy comfort level until they take a full inventory of their contacts and their level of intimacy, at which point their privacy has been compromised beyond repair.
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