This week, I read the article 'Towards Predicting the Best Answers in Community Based Question-Answering Services' and presented the material to our group. The study analyzed a large dataset from StackOverflow based on answer content for a random sample of questions being asked before August of 2012. It was looking to predict if an answer may be selected as the best based on classifier learning from labeled data. The results showed that the more answers from users on StackOverflow an original answer has, the less likely it is to be selected as the best, and that what qualifies a best answer is one with more details and a clear explanation of a solution. Typically, answers that provide an in depth solution would rank the best, and sure enough, users who were not the first to answer the posed question ranked 70.71% accurate.
I also read through our task list that Ali created and came up with tags that I thought worked best with the questions being asked on StackOverflow. Once our tasks are finalized, we will be using our empirical studies lab to gather participants and ask them to predict tags of their own, or choose from our list of five.
No comments:
Post a Comment