Publication Type

Conference Proceeding Article

Publication Date

8-2013

Abstract

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.

Keywords

Communities, Social network services, Principal component analysis, Correlation, Conferences, Standards, Educational institutions

Discipline

Databases and Information Systems | Theory and Algorithms

Publication

Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on

First Page

1387

Last Page

1394

Identifier

10.1145/2492517.2500293

Publisher

IEEE

City or Country

New York, US

Copyright Owner and License

LARC

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/2492517.2500293

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