Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Features for image retrieval: an experimental comparison
Information Retrieval
Identifying authoritative actors in question-answering forums: the case of Yahoo! answers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to recognize reliable users and content in social media with coupled mutual reinforcement
Proceedings of the 18th international conference on World wide web
Design lessons from the fastest q&a site in the west
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Modeling answerer behavior in collaborative question answering systems
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Early detection of potential experts in question answering communities
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Modeling problem difficulty and expertise in stackoverflow
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work Companion
Information Processing and Management: an International Journal
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Collaborative Web applications, such as forums and question answering websites, help users to get answers to a wide array of questions. Given the large amount of information available, it is important to devise automatic methods that surface high quality answers. Our objective here is to determine if there are any particular aspects of a user's profile or activity in the community that can be exploited to spot high quality contributions. We first perform an in-depth analysis of the information provided by the users in their profiles in order to discriminate features that are correlated to expertise. Then, we propose an answer ranking scenario in which we assess the predictive capabilities of profile and activity related features. In our experiments, we use a large scale corpus from Stackoverflow, a very active Q&A community focused on technical topics and find that answer rankings obtained using a user model outperform a ranking based on the chronological order of answers.