A brief survey of computational approaches in social computing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
We know who you followed last summer: inferring social link creation times in twitter
Proceedings of the 20th international conference on World wide web
Human computation: a survey and taxonomy of a growing field
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CrowdDB: answering queries with crowdsourcing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Proceedings of the VLDB Endowment
Twitter catches the flu: detecting influenza epidemics using Twitter
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Max algorithms in crowdsourcing environments
Proceedings of the 21st international conference on World Wide Web
Active surveying: a probabilistic approach for identifying key opinion leaders
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
CrowdER: crowdsourcing entity resolution
Proceedings of the VLDB Endowment
Whom to ask?: jury selection for decision making tasks on micro-blog services
Proceedings of the VLDB Endowment
Choosing the right crowd: expert finding in social networks
Proceedings of the 16th International Conference on Extending Database Technology
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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.