Photo credit: Konstantinos Kokkinis
In 1967, American psychologist Stanley Milgram conducted the "small world experiment", in which he sent letters to sixty volunteers in Kansas and asked them to forward the envelopes to a specific person in Massachusetts—by hand and through friends or friends of friends.
The letters that reached the addressee were, on average, relayed by five to seven people. This is seen as an empirical proof that arbitrary people in our society are related to each other through friends and friends of friends. The small world hypothesis based on Milgram's findings states that the number of personal acquaintances needed to connect two random persons on the planet is small.
The hypothesis led to the expression "the six degrees of separation", meaning that any two random persons are associated with each other by a chain of about six individuals. The "six degrees of separation" is one of the underlying concepts of social networks on the Internet.
Social networking services offer friends a space where they can maintain their relationships, chat with each other and share information. Moreover, they offer the opportunity to build new relationships through existing friends. On the first use of the system, users are required to submit a profile containing personal information such as their name, date of birth, and a photo. The personal information is made available to other users of the system, and is used to identify friends on the network and to add them to a list of contacts.
Photo credit: Saumier
In most systems, users can not only view their friends but also second degree friends (friends of their friends). Some networks follow an "invitation only" approach. Hence, every person in the system is automatically connected to at least one other person. Examples for common social networks are Friendster with about 24 million users, MySpace with about 41 million users, and Google's Orkut with about 12 million users.
In addition to these general-purpose networks, specialised services have evolved in order to establish a community of like-minded individuals. OpenBC, for instance, is a professional networking service that attempts to create a web of trusted experts and business partners.
When communities grow larger, self-organisation tendencies emanate, and frequently sub-communities covering more specific topics or smaller groups of friends are established. Several services including Orkut facilitate creating new sub-groups as a core functionality of the system. In these smaller communities users chat, have lively discussions in dedicated forums, and exchange pictures and other documents.
The formation of smaller groups within a large collective can probably be described with the rule of 150. This axiom refers to the social channel capacity, the ability of the human brain to relate factual, emotional, and social details to people. A series of social studies show that the average person can remember these features for approximately 150 individuals.
Psychologists explain this characteristic by using the evolution of human societies: early settlements did not comprise more than 100-150 people, and therefore the brain developed only to the point where it was able to store the information on all people in this social network. Thus, a "genuine" social network is limited to about 150 people.
Most popular social networks in use ask users explicitly for personal information. Hence users fill out profiles and provide personal data as well details on their likes and dislikes.
As mentioned above, users add their friends manually to the list of contacts. So the social network is generated manually, which usually results in a high accuracy of the connections made. A system that forms a large social network without the users' explicitly knowing it, although users provide the required information voluntarily, is Skype.
Skype is a provider of free internet telephony. Every user in the system has a user profile that can contain the name, address, phone number, e-mail address, a photo, etc. When person A wants to call person B, usually the profile of person B is added to the contacts of person A. Calling a person is a strong indication of a personal or professional relationship. Thus, the information stored in Skype represents a large, manually generated social network.
An alternative approach to manual generation relies on fully automatic creation of networks. E-mails of a group of users, their postings in newsgroups and blogs, links on their homepages and similar resources are analysed. An e-mail from user A to user B, for instance, indicates a connection between users A and B. In the same way, a follow-up by user B to a newsgroup posting by user A can be interpreted as a (weak) relation between the two users.
All connections detected by the generative algorithm are accumulated and utilised to form a graph of weighted edges between "user nodes". Edges whose weight is over a given threshold correspond to the connections in the network to be generated. The advantage of this method is that it does not require user interaction.
Moreover, it is capable of unveiling connections that might otherwise have remained implicit or hidden. The drawback is, however, that automatic generation of the network cannot be as precise as manually adding contacts. Furthermore, a fully automated process is usually not able to collect the personal data provided by users.
Automatic generation of a type of "social networks" is also possible for services such as eBay or Amazon. In eBay, for instance, information is retained on who bought from whom, which buyer rated which seller, etc. This information can be used to generate a network of weighted connections, where the weight depends on positive, neutral or negative ratings between buyers and sellers.
In Amazon, users' buying a book, writing a review or giving a recommendation for a book imply that they have an interest in the author or the topic. Although this data does not form a traditional social network, it can be interpreted as a social structure in the broader sense. On the one hand, clusters of users with similar interests are formed, and clusters and users are connected with each other; a friendly contact and direct communication among users is, however, not possible.
Use of Social Networks
The obvious aim of social networks is to give users a way to stay in touch with friends, colleagues, and acquaintances. Services such as OpenBC also let users browse through their contacts and second degree contacts (contacts of contacts).
Additionally, users in OpenBC can search for people with certain skills or other attributes. When an appropriate person is found, the chain of contacts to this person is displayed. Thus, users can, for example, ask their friends and friends' friends on the person's qualification. Potentially, one of the biggest application areas of social networks might be personalised searching on the World Wide Web.
Whereas today's search engines provide largely anonymous or uncredited information, future versions might highlight or recommend web pages created by recognised or familiar individuals. The integration of search engines and social networks could also enable queries such as "Has any of my acquaintances been on holidays in New Zealand?" or "Recent articles on hypertext authored by people associated with Ted Nelson".
It should be noted that real concerns regarding the privacy of members of social networks exist. Information on consumers that privacy activists have been trying to protect from companies are nowadays provided willingly by inexperienced users. The detailed personal information stored in user profiles, for instance, could be utilised to send disseminate personalised fraudulent advertisements, automatically sign users up to services matching their profiles or even sell the personal data to third parties.
Moreover, the service providers have the ability to monitor and store the information communicated among users and make use of ideas expressed and data transferred during users' discussions.
Other Community-Based Networks
Although not directly associated with social networks, this section introduces three community-based networking services: del.icio.us, Furl, and Eurekster.
del.icio.us is a social bookmarking and classification service that enables collecting and sharing favourite web pages. Users can add bookmarks of web pages to del.icio.us, attach tags or keywords and choose if it is to be publicly available or private. The keywords assigned by users are used for non-hierarchical categorisation of the bookmarks. Hence, clusters of bookmarks for various topics are created in the system.
When users access a bookmark, they can also look at the public bookmarks of users that have the same web page in their portfolio. Moreover, users can search for "similar" pages—bookmarks that share certain keywords or are in the same bookmark cluster.
Furl, a similar service, takes the concept a step further and stores bookmarked articles in an internal database. Thus, users can create their own "Personal Web" that only contains the pages they store. As in del.icio.us, pages can be private or publicly accessible. In addition to keywords users can also assign topics, give ratings and attach comments to pages.
Furl also creates an index of all documents stored in its database and offers full-text search functionality. Both Wikipedia and services like del.icio.us are employed by some users as an alternative to conventional search engines such as Google.
Wikipedia is a good starting point for many topics, since it can give an overview of a topic and frequently offers manually selected links to more detailed resources. Similarly, a query in del.icio.us yields a number of web pages that have been selected by users as one of their favourite pages on the Web. Although Google's search and ranking algorithms are very sophisticated and mostly offer relevant results first, they are currently unable to offer documents that were evaluated and chosen by individuals.
Eurekster is a collaborative search engine whose concept is a blend of social networking and social bookmarking. People sign up to the system and form communities of users with similar interests. When a user searches the Web, information on the query and the documents actually chosen from the result are stored in the system.
These data are used in order to introduce a prioritisation of topics within the community and perform a ranking of relevant articles within a topic. Thus, the system eventually "knows" which topics and web pages are relevant for a community. A user part of a community of archaeologists searching for adobe, for instance, might be confronted with results including historic sites in Peru. By contrast, in a technology centred community documents on the computer software company Adobe might be the result of the query.
End of Part VI of 7
Read Part I: Blogs, Wikis, Podcasting, Social Networks And File Sharing: How The Web Is Transforming Itself
Read Part II: Introduction To Blogs - How The Web Is Transforming Itself
Read Part III: Introduction To Wikis: How The Web Is Transforming Itself
Read Part IV: Introduction To Wikipedia And WikiNews: How The Web Is Transforming Itself
Read Part V: Podcasting and File Sharing: How The Web Is Transforming Itself
Read Part VI: Social Networks and Social Services: How The Web Is Transforming Itself
Next part: The Trasformation of the Web - Overview and Conclusions
Originally published as "The Transformation of the Web: How Emerging Communities Shape the Information we Consume", on jucs.org by Josef Kolbitsch (Graz University of Technology, Austria), and Hermann Maurer (Institute for Information Systems and Computer Media, Graz University of Technology, Austria) on August, 2006
About the authors
Josef Kolbitsch holds a PhD in computer science from the Institute of Information Systems and Computer Media, Graz University of Technology, Austria. He has conducted Several projects in the area of web-based database systems, information processing and information management systems for organisations including the Association of Telematic Engineers, the Association of Austrian Business Engineers, Graz University of Technology, and Lebenshilfe Steiermark. In addition he has been the Software trainer and personal technical trainer for Berufsförderungsinstitut Steiermark and Symantec Corporation(Auckland Branch), software license manager for Graz University of Technology, and an honorary research assistant at the Department of Computer Science, University of Auckland, New Zealand.
Contact Information: josef.kolbitsch(at)tugraz.at
Hermann Maurer holds a PhD in Mathematics from the University of Vienna. He has been teaching at various universities since 1966, and has been the director of the Research Institute for Applied Information Processing of the Austrian Computer Society 1983-1998; chairman of Institute for Information Processing and Computer Supported New Media since 1988, director of the Institute for Hypermedia Systems of Joannum research since 1990, director of the AWAC (Austrian Web Application Center) of the ARCS (Austrian Research Centers) 1997-2000, member of the board of OCG (Österreische Computergesellschaft) 1979-2003, founder and scientific advisor of the KNOW Center (K+ Center), the first research centre on Knowledge Management in Austria. Since January 2004 Hermann Maurer is the first dean of the newly formed Faculty for Computer Science at the Graz University of Technology.
Contact Information: hmaurer(at)iicm.edu
Josef Kolbitsch and Hermann Maurer -
Photo credits: Large Communities and Use of Social Networks - Photo credit: Konstantinos Kokkinis
Technical Aspects - Photo credit: Pekka Jaakkola
Reference: Journal of Universal Computer Science [ Read more ]