Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Discovering authorities in question answer communities by using link analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Questions in, knowledge in?: a study of naver's question answering community
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 4th International Conference on Persuasive Technology
Expert identification in community question answering: exploring question selection bias
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Design lessons from the fastest q&a site in the west
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automatic identification of best answers in online enquiry communities
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
The 24th ACM Conference on Hypertext and Social Media (HT2013): a personal review
ACM SIGWEB Newsletter
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Online enquiry communities such as Question Answering (Q&A) websites allow people to seek answers to all kind of questions. With the growing popularity of such platforms, it is important for community managers to constantly monitor the performance of their communities. Although different metrics have been proposed for tracking the evolution of such communities, maturity, the process in which communities become more topic proficient over time, has been largely ignored despite its potential to help in identifying robust communities. In this paper, we interpret community maturity as the proportion of complex questions in a community at a given time. We use the Server Fault (SF) community, a Question Answering (Q&A) community of system administrators, as our case study and perform analysis on question complexity, the level of expertise required to answer a question. We show that question complexity depends on both the length of involvement and the level of contributions of the users who post questions within their community. We extract features relating to askers, answerers, questions and answers, and analyse which features are strongly correlated with question complexity. Although our findings highlight the difficulty of automatically identifying question complexity, we found that complexity is more influenced by both the topical focus and the length of community involvement of askers. Following the identification of question complexity, we define a measure of maturity and analyse the evolution of different topical communities. Our results show that different topical communities show different maturity patterns. Some communities show a high maturity at the beginning while others exhibit slow maturity rate.