CrowdMGR : Interactive Visual Analytics to Interpret Crowdsourced Data

Crowdsourcing is popularly defined as a paradigm that utilizes human processing power to solve problems that computers cannot yet solve. While recent research has been dedicated to improve the problem-solving potential of crowdsourcing activities, not much has been done to help a user quickly extract the valuable knowledge from crowdsourced solutions to a problem, without having to spend a lot of time examining all content in details. Online knowledge-sharing forums (Y! Answers, Quora, and StackOverflow), review aggregation platforms (Amazon, Yelp, and IMDB), etc. are all instances of crowdsourcing sites which users visit to find out solutions to problems. In this paper, we build a system CrowdMGR that performs visual analytics to help users manage and interpret crowdsourced data, and find relevant nuggets of information. Given a user query (i.e., a problem), CrowdMGR returns the solution, referred to as the SolutionGraph, to the problem as an interactive canvas of linked visualizations. The SolutionGraph allows a user to systematically explore, visualize and extract the knowledge in the crowdsourced data. It not only summarizes content directly linked to a user’s query, but also enables her to explore related topics within the temporal and topical scope of the query and discover answers to questions which she did not even ask. In the demonstration, participants are invited to manage and interpret crowdsourced data in StackOverflow and Computer Science Stack Exchange, question and answer site for students, researchers and practitioners of computer science.