Data Science-Driven Crowd Intelligence and Its Business Applications

Abstract

Crowd intelligence has shown to be a successful practice in various traditional applications such as the opinion poll. Over the last decades, the proliferation of the Internet has created a group of even more “intelligent” crowds which are more massive, versatile, elastic, and can be accessed immediately at a lower cost. Tapping into such crowds has triggered the emergence of many new business applications and provides unprecedented opportunities for existing businesses to innovate and improve their practices. Despite the promise, there are still lots of managerial concerns to address in order to successfully distill the wisdom of crowds to create business values. In a typical crowd intelligence scenario, the major concerns include what kind of crowd intelligence to use, how to extract or collect crowd intelligence, and how to aggregate or evaluate the crowd intelligence. In this dissertation, I use data science-driven approaches that leverage deep learning techniques, Bayesian graphical models, and their combinations, to address these concerns. Three essays are included in this dissertation. The first essay designs an interactive attention-based deep learning approach to extract crowd opinions from social media. The second essay explores how to aggregate the noisy crowd intelligence in the crowdsourcing scenario by designing a deep generative model. The last essay taps into the crowd intelligence to tackle the current false news crisis. This dissertation not only illustrates how we can take advantage of crowd intelligence in various real-world applications but also guides future computational design science research in the Information System (IS) field.