Demographic data mining is a recent enterprise in social networking on the internet and appears to be still in its infancy. Social web sites commonly ask the user to create a profile to help them find friends, that is the like-minded they wish to communicate with. The collected personal information is used to define the users' context of life and attempt to match it against that of other users. In my experience, the most widely-applied data mining strategies quarry only the simplest information, e.g. residence, school or employer, and only the most fundamental personal characteristics are examined. That is, age, gender and marital status are used to draw conclusions about the interests and preferences of the profiled.
In addition to providing matches for users, these data are used to steer advertisements to the users' web pages that inform on products and services they may be interested in. This seems only fair, because the service on the web site is provided free of charge. However, the conclusions drawn from the users' profile seem at times unrefined and presumptuous, painting a startling image of the average person.
I am 53, male and married. I provide these demographics in my profiles. On my page on one popular social network site, I recently found advertisements of services entitled "Dating for Seniors" and "Meet Married Women in your Neighborhood". Welcome to the real world? A look at my profile however suggests that I am not a likely consumer of these services. Such misguided targeting may only convince users to develop avartars, i.e. virtual cyberspace personalities, bearing little resemblance with their actual lives. Credibility suffers, while professional advertising agencies must be interested in finding true clients for their customers. Obviously, there is ample space for improvement. Until more suitable targeting has been developed, we must tolerate these inconveniences with a . They are minor compared with the great opportunities social sites offers.