Lingfei Wu and Zi-gang Huang
| Online communities are becoming increasingly important as platforms for knowledge production. In these communities users seek and share professional skills, spreading knowledge along the hierarchy of expertise levels. To investigate how users collaborate with each other in knowledge production, we analyze StackExchange, one of the largest question and answer systems in the world. Our dataset includes the asking and answering activities of 2.7 million users over 5 years across 110 communities. We construct expertise networks to include all pairs of help-seeking interactions and measure the expertise level Li of the ith user based on their positions in expertise networks. We calculate * | Lj-Li | * between all pairs of linked users i and j and suggest that, this variable characterizes the cost of attention users are willing to pay in helping others, because it is reasonable to assume that the communication between users across more expertise levels usually takes more efforts. We find that the distribution of * | Lj-Li | * is symmetrical and unimodal, with a small mean and a small standard deviation. This means that users at all expertise levels tend to help those whose levels are slightly below themselves. In other words, people do not go very far out of their “comfort zones” to help others. This observation explains the forming of hierarchy in large-scale knowledge production and provide an important guidance rule for building expert recommendation systems. |