Bullipedia, the online gastronomic encyclopedia, is an idea yet to be developed. In this work, we analyze Stack Overflow (SO) and extract some good practices from this popular question-and-answer (Q&A) site to incorporate them into the future Bullipedia. SO is an online forum in which users ask and answer questions related to programming, web development, operating systems, and other technical topics. Expertise is rewarded through a detailed reputation system: questions and answers can receive up and downvotes from other members of the community so that their authors (askers and answerers) gain reputation for posting good questions and providing helpful solutions. Besides this, the asker may mark (accept) one of the answers as the best one at any point. In this paper, we present a study on how this reputation system can be used to predict the likely accepted answer (from a set of candidate answers) for a yet unresolved question.
Community Engagement and Quality Knowledge with Stackoverflow’s Reputation System: Applications to Bullipedia
January 23, 2018
Digital Studies
Research Paper
Bullipedia, classification, machine learning, online community, Q&A site, reputation, StackOverflow, trust
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Abstract
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