This study assesses the feasibility of identifying self-reported sports practitioners (soccer players) on Facebook. The main goal is to develop a system to support marketers with the decision as to which prospects to target for advertising purposes. To do so, we benchmark several algorithms (i.e., random forest, logistic regression, adaboost, rotation forest, neural networks, and kernel factory) using five times twofold cross-validation. To evaluate performance and variable importances, we build a fusion model, which combines the results of the other algorithms using the weighted average. This technique is also referred to as information-fusion sensitivity analysis. The results reveal that Facebook data provide a viable basis to come up with sports predictions as the predictive performance ranges from 72.01% to 80.43% for area under the receiver operating characteristic curve (AUC), from 81.96% to 83.95% for accuracy, and from 2.41 to 3.06 for top-decile lift. Our benchmark study indicates that stochastic...

中文翻译：

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通过预测分析识别Facebook上的足球运动员

这项研究评估了在Facebook上识别自我报告的体育从业者（足球运动员）的可行性。主要目标是开发一种系统，以支持营销人员决定将哪些潜在客户定位为广告目的。为此，我们使用五次双重交叉验证对几种算法（即随机森林，逻辑回归，adaboost，旋转森林，神经网络和内核工厂）进行基准测试。为了评估性能和重要性，我们建立了一个融合模型，该模型使用加权平均值结合了其他算法的结果。此技术也称为信息融合敏感性分析。结果显示，Facebook数据为体育预测提供了可行的基础，因为预测性能介于72.01％至80之间。接收器工作特性曲线（AUC）下面积的43％，精度从81.96％到83.95％，最高十进制升力从2.41到3.06。我们的基准研究表明，随机...