Crowd-Squared: Amplifying the Predictive Power of Search Trend Data1
提出一种名为“众包平方”的结构化方法，利用众包从搜索趋势数据中筛选关键术语，在两个领域的实证测试中预测效果优于或等同于现有方法，对使用搜索数据进行预测的研究者和从业者有参考价值。
Big data generated by crowds provides a myriad of opportunities for monitoring and modeling people’s intentions, preferences, and opinions. A crucial step in analyzing such big data is selecting the relevant part of the data that should be provided as input to the modeling process. In this paper, we offer a novel, structured, crowd-based method to address the data selection problem in a widely used and challenging context: selecting search trend data. We label the method “crowd-squared,” as it leverages crowds to identify the most relevant terms in search volume data that were generated by a larger crowd. We empirically test this method in two domains and find that our method yields predictions that are equivalent or superior to those obtained in previous studies (using alternative data selection methods) and to predictions obtained using various benchmark data selection methods. These results emphasize the importance of a structured data selection method in the prediction process, and demonstrate the utility of the crowd-squared approach for addressing this problem in the context of prediction using search trend data.