The suitability of crowdsourcing to solve a variety of problems has been investigated widely. Yet, there is still a lack of understanding about the distinct behavior and performance of workers within microtasks. In this paper, we first introduce a fine-grained data-driven worker typology based on different dimensions and derived from behavioral traces of workers. Next, we propose and evaluate novel models of crowd worker behavior and show the benefits of behavior-based worker pre-selection using machine learning models. We also study the effect of task complexity on worker behavior. Finally, we evaluate our novel typology-based worker pre-selection method in image transcription and information finding tasks involving crowd workers completing 1,800 HITs. Our proposed method for worker pre-selection leads to a higher quality of results when compared to the standard practice of using qualification or pre-screening tests. For image transcription tasks our method resulted in an accuracy increase of nearly 7% over the baseline and of almost 10% in information finding tasks, without a significant difference in task completion time. Our findings have important implications for crowdsourcing systems where a worker’s behavioral type is unknown prior to participation in a task. We highlight the potential of leveraging worker types to identify and aid those workers who require further training to improve their performance. Having proposed a powerful automated mechanism to detect worker types, we reflect on promoting fairness, trust and transparency in microtask crowdsourcing platforms.

中文翻译：

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超越好与坏的人群解剖：用于人群工作者建模和预选的行为痕迹

众包解决各种问题的适用性已被广泛研究。然而，对于微任务中工人的独特行为和绩效仍然缺乏了解。在本文中，我们首先介绍一种基于不同维度的细粒度数据驱动型工人类型，并从工人的行为痕迹中得出。接下来，我们提出并评估人群工人行为的新颖模型，并使用机器学习模型展示基于行为的工人预选的好处。我们还研究了任务复杂性对工人行为的影响。最后，我们在涉及人群工人完成1800个HIT的图像转录和信息查找任务中，评估了我们基于类型学的新型工人预选方法。与使用资格认证或预筛选测试的标准做法相比，我们建议的工人预选方法可提高结果质量。对于图像转录任务，我们的方法导致精度比基线提高了近7％，而信息查找任务的精度提高了近10％，而任务完成时间没有显着差异。我们的发现对众包系统具有重要的意义，在众包系统中，工人的行为类型在参与任务之前是未知的。我们强调了利用工人类型来识别和帮助需要进一步培训以改善其绩效的工人的潜力。在提出了一种强大的自动化机制来检测工人类型之后，我们反思了如何在微任务众包平台中促进公平，信任和透明。