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.

Cette thèse s'intéresse particulièrement aux sites de vente en ligne et à leurs réseaux sociaux. La propension des utilisateurs utiliser ces sites Web tels qu'eBay et Amazon est de plus en plus importante en raison de leur fiabilité. Les consommateurs se réfèrent à ces sites Web pour leurs besoins et en deviennent clients. L'un des défis à relever est de fournir les informations utiles pour aider les clients dans leurs achats. Ainsi, une question sous-jacente à la thèse cherche à répondre est de savoir comment fournir une information complète aux clients afin de les aider dans leurs achats. C'est important pour les sites d'achats en ligne car cela satisfait les clients par ces informations utiles. Pour surmonter ce problème, trois études spécifiques ont été réalisées : (1) Trouver les utilisateurs influents, (2) Comprendre la propagation d'opinion et (3) Agréger les opinions. Dans la première partie, la thèse propose une méthodologie pour trouver les utilisateurs influents du réseau qui sont essentiels pour une propagation précise de l'opinion. Pour ce faire, les utilisateurs sont classés en fonction de deux scores : optimiste et pessimiste. Dans la deuxième partie, une nouvelle méthodologie de propagation de l'opinion est présentée pour parvenir à un accord et maintenir la cohérence entre les utilisateurs, ce qui rend l'agrégation possible. La propagation se fait en tenant compte des impacts des utilisateurs influents et des voisins. Enfin, dans la troisième partie, l'agrégation des avis est proposée pour rassembler les avis existants et les présenter comme des informations utiles pour les clients concernant chaque produit du site de vente en ligne. Pour ce faire, l'opérateur de calcul de la moyenne pondérée et les techniques floues sont utilisées. La thèse présente un modèle d'opinion consensuelle dans les réseaux. Les travaux peuvent s'appliquer à tout groupe qui a besoin de trouver un avis parmi les avis de ses membres. Par conséquent, le modèle proposé dans la thèse fournit un taux précis et approprié pour chaque produit des sites d'achat en ligne / Online Social Networks are increasing and piercing our lives such that almost every person in the world has a membership at least in one of them. Among famous social networks, there are online shopping websites such as Amazon, eBay and other ones which have members and the concepts of social networks apply to them. This thesis is particularly interested in the online shopping websites and their networks. According to the statistics, the attention of people to use these websites is growing due to their reliability. The consumers refer to these websites for their need (which could be a product, a place to stay, or home appliances) and become their customers. One of the challenging issues is providing useful information to help the customers in their shopping. Thus, an underlying question the thesis seeks to answer is how to provide comprehensive information to the customers in order to help them in their shopping. This is important for the online shopping websites as it satisfies the customers by this useful information and as a result increases their customers and the benefits of both sides. To overcome the problem, three specific connected studies are considered: (1) Finding the influential users, (2) Opinion Propagation and (3) Opinion Aggregation. In the first part, the thesis proposes a methodology to find the influential users in the network who are essential for an accurate opinion propagation. To do so, the users are ranked based on two scores namely optimist and pessimist. In the second part, a novel opinion propagation methodology is presented to reach an agreement and maintain the consistency among users which subsequently, makes the aggregation feasible. The propagation is conducted considering the impacts of the influential users and the neighbors. Ultimately, in the third part, the opinion aggregation is proposed to gather the existing opinions and present it as the valuable information to the customers regarding each product of the online shopping website. To this end, the weighted averaging operator and fuzzy techniques are used. The thesis presents a consensus opinion model in signed and unsigned networks. This solution can be applied to any group who needs to find a plenary opinion among the opinions of its members. Consequently, the proposed model in the thesis provides an accurate and appropriate rate for each product of the online shopping websites that gives precious information to their customers and helps them to have a better insight regarding the products.