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. In our approach, we selected a subset of questions with their respective answers, and for each answer we created a question-answer pair (quan). Then we extracted a set of key features from every quan, and applied supervised machine learning techniques to train a classifier that learnt, based on those features, whether or not a quan contained the accepted answer for that question. Finally, we made use of the trained classifier to predict if, given a quan (related to a question with no marked answer), its answer might potentially be the accepted one for the question. Our findings show that the model previously obtained predicted the possible answer correctly for every question with high accuracy (88 percent of the time). A question and its accepted answer constitutes a source of quality knowledge, as it provides the solution to a specific problem. We propose to adopt a similar Q&A forum and reputation system for Bullipedia, and then apply a similar classification model to identify the best answer for unsolved questions.
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