“CrowdIQ: A New Opinion Aggregation Model” by Dr. G. Alan Wang
Dr. G. Alan Wang
Associate Professor in Business Information Technology
Pamplin College of Business
In this study, we investigate the problem of aggregating crowd opinions for decision making. According to the Wisdom of Crowds (WoC) theory, crowd independence and the way in which individual judgements are aggregated are two important factors to crowd wisdom. Most existing crowd opinion aggregation methods fail to build a differential weighting mechanism for identifying individual expertise and appropriately accounting for crowd dependence when aggregating their judgments. We propose a new crowd opinion aggregation model, namely CrowdIQ, that has a differential weighting mechanism and accounts for dependence among individual judgements. We conduct experiments to evaluate CrowdIQ in comparison to four baseline methods using real data collected from an online stock investment community, StockTwits. The results show that CrowdIQ significantly outperforms all baseline methods in terms of both a quadratic prediction scoring measure and simulated investment returns.