Conference Proceeding Article
In this paper, we analyze the network of expertise constructed from the interactions of users on the online questionanswering (QA) community of Stack Overflow. This community was built with the intention of helping users with their programming tasks and, thus, questions are expected to be highly factual. This also indicates that the answers one provides may be highly indicative of one's level of expertise on the subject matter. Therefore, our main concern is how to model and characterize the user's expertise based on the constructed network and its centrality measures. We used the user's reputation established on Stack Overflow as a direct proxy to their expertise. We further made use of linear models and principal component analysis for the purpose. We found out that the current reputation system does a decent job at representing the user's expertise and that focus matters when answering factual questions. However, our model was not able to capture the other larger half of reputation which is specifically designed to reflect a user's trustworthiness besides their expertise. Along the way, we also discovered facts that have been known in earlier studies of the other/same QA communities such as the power-law degree distribution of the network and the generalized reciprocity pattern among its users. Copyright 2013 ACM.
Communities, Social network services, Principal component analysis, Correlation, Conferences, Standards, Educational institutions
Databases and Information Systems | Theory and Algorithms
Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on
City or Country
New York, US
LE TRUC VIET and NGUYEN, Minh Thap.
An empirical analysis of a network of expertise. (2013). Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on. 1387-1394. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3468
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.