Currently, crowdsourced query processing is done on reward-driven platforms such as Amazon Mechanical Turk (AMT) and CrowdFlower. However, due to budget constraints for conducting a crowdsourcing task in practice, the scalability is inherently poor. In this thesis, we exploit microblogs for supporting crowdsourced query processing. We leverage the social computation power and decentralize the evaluation of the crowdsourcing platforms queries towards social networks. We propose a new problem of minimizing the cost of processing crowdsourced queries on microblogs, given a specied accuracy threshold of users' votes. This problem is NP-hard and its computation is #P-hard. To tackle this problem, we develop a greedy algorithm with a quality guarantee. We demonstrate the performance o...[ Read more ]

Currently, crowdsourced query processing is done on reward-driven platforms such as Amazon Mechanical Turk (AMT) and CrowdFlower. However, due to budget constraints for conducting a crowdsourcing task in practice, the scalability is inherently poor. In this thesis, we exploit microblogs for supporting crowdsourced query processing. We leverage the social computation power and decentralize the evaluation of the crowdsourcing platforms queries towards social networks. We propose a new problem of minimizing the cost of processing crowdsourced queries on microblogs, given a specied accuracy threshold of users' votes. This problem is NP-hard and its computation is #P-hard. To tackle this problem, we develop a greedy algorithm with a quality guarantee. We demonstrate the performance on real data sets.