Enabled by the wave of online crowdsourcing activities, extracting the Wisdom of Crowds (WoC) has become an emerging research area, one that is used to aggregate judgments, opinions, or predictions from a large group of individuals for improved decision making. However, existing literature mostly focuses on eliciting the wisdom of crowds in an offline context—without tapping into the vast amount of data available on online crowdsourcing platforms. To extract WoC from participants on online platforms, there exist at least three challenges, including social influence, suboptimal aggregation strategies, and data sparsity. This dissertation aims to answer the research question of how to effectively extract WoC from crowdsourcing platforms for the purpose of making better decisions. In the first study, I designed a new opinions aggregation method, Social Crowd IQ (SCIQ), using a time-based decay function to eliminate the impact of social influence on crowd performance. In the second study, I proposed a statistical learning method, CrowdBoosting, instead of a heuristic-based method, to improve the quality of crowd wisdom. In the third study, I designed a new method, Collective Persuasibility, to solve the challenge of data sparsity in a crowdfunding platform by inferring the backers' preferences and persuasibility. My work shows that people can obtain business benefits from crowd wisdom, and it provides several effective methods to extract wisdom from online crowdsourcing platforms, such as StockTwits, Good Judgment Open, and Kickstarter. / Doctor of Philosophy / Since Web 2.0 and mobile technologies have inspired increasing numbers of people to contribute and interact online, crowdsourcing provides a great opportunity for the businesses to tap into a large group of online users who possess varied capabilities, creativity, and knowledge levels. Howe (2006) first defined crowdsourcing as a method for obtaining necessary ideas, information, or services by asking for contributions from a large group of individuals, especially participants in online communities. Many online platforms have been developed to support various crowdsourcing tasks, including crowdfunding (e.g., Kickstarter and Indiegogo), crowd prediction (e.g., StockTwits, Good Judgment Open, and Estimize), crowd creativity (e.g., Wikipedia), and crowdsolving (e.g., Dell IdeaStorm). The explosive data generated by those platforms give us a good opportunity for business benefits. Specifically, guided by the Wisdom of Crowds (WoC) theory, we can aggregate multiple opinions from a crowd of individuals for improving decision making. In this dissertation, I apply WoC to three crowdsourcing tasks, stock return prediction, event outcome forecast, and crowdfunding project success prediction. Our study shows the effectiveness of WoC and makes both theoretical and practical contributions to the literature of WoC.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/102032 |
| Date | 02 August 2019 |
| Creators | Du, Qianzhou |
| Contributors | Management, Wang, Gang Alan, Khansa, Lara Z., Seref, Onur, Fan, Weiguo, Russell, Roberta S. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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