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WiseMarket: A New Paradigm for Managing Wisdom of Online Social Users

WiseMarket: A New Paradigm for Managing Wisdom of Online Social Users

This video was recorded at 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Chicago 2013. The benefits of crowdsourcing are well-recognized today for an increasingly broad range of problems. Meanwhile, the rapid development of social media makes it possible to seek the wisdom of a crowd of targeted users. However, it is not trivial to implement the crowdsourcing platform on social media, specifically to make social media users as workers, we need to address the following two challenges: 1) how to motivate users to participate in tasks, and 2) how to choose users for a task. In this paper, we present Wise Market as an effective framework for crowdsourcing on social media that motivates users to participate in a task with care and correctly aggregates their opinions on pairwise choice problems. The Wise Market consists of a set of investors each with an associated individual confidence in his/her prediction, and after the investment, only the ones whose choices are the same as the whole market are granted rewards. Therefore, a social media user has to give his/her ``best'' answer in order to get rewards, as a consequence, careless answers from sloppy users are discouraged. Under the Wise Market framework, we define an optimization problem to minimize expected cost of paying out rewards while guaranteeing a minimum confidence level, called the Effective Market Problem (EMP). We propose exact algorithms for calculating the market confidence and the expected cost with O(nlog2n) time cost in a Wise Market with n investors. To deal with the enormous number of users on social media, we design a Central Limit Theorem-based approximation algorithm to compute the market confidence with O(n) time cost, as well as a bounded approximation algorithm to calculate the expected cost with O(n) time cost. Finally, we have conducted extensive experiments to validate effectiveness of the proposed algorithms on real and synthetic data.


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