Recently, a new class of search applications that support collaborative information seeking has emerged. In these systems, users work in small groups with a shared information need, rather than relying on large numbers of anonymous users with potentially diverging information needs. One clear way to distinguish different social search activities has been proposed by Colum Foley. In his PhD thesis, he characterizes search systems on two dimensions, “Sharing of Knowledge” and “Division of labor.” Sharing of knowledge separates all social search systems from traditional single-user approaches, while division of labor separates social search from collaborative search.
In social search systems such as those described by Ed Chi in his blog post, knowledge is shared implicitly by aggregating the behaviors or opinions of many people. There isn’t much division of labor because each new user has to establish patterns of behavior similar to others’ to benefit from the others’ activities. Furthermore, each person has to vet the recommendations to make sure they are appropriate to that person’s information need. Collaborative search systems, on the other hand, can be characterized by high division of labor. Information found by one team member is made available to other team members without requiring additional work. Thus social search systems tend to be effective at improving retrieval of common documents (the “big head”), while collaborative search systems tend to be more effective at identifying novel or unusual information (the “long tail”).