A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality

TL;DR

This study analyzes long-term worker quality in crowdsourcing, revealing stability over time and the ability to predict future performance from early tasks, challenging assumptions of fatigue or satisficing.

Contribution

It provides the first large-scale longitudinal analysis of crowd worker quality and demonstrates that early performance can reliably predict long-term quality.

Findings

Workers maintain consistent quality over extended periods.

Workers do not adjust quality based on acceptance thresholds.

Early task performance predicts long-term quality effectively.

Abstract

Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets. As a result, crowd workers spend weeks or months repeating the exact same tasks, making it necessary to understand their behavior over these long periods of time. We utilize three large, longitudinal datasets of nine million annotations collected from Amazon Mechanical Turk to examine claims that workers fatigue or satisfice over these long periods, producing lower quality work. We find that, contrary to these claims, workers are extremely stable in their quality over the entire period. To understand whether workers set their quality based on the task's requirements for acceptance, we then perform an experiment where we vary the required quality for a large crowdsourcing task. Workers did not adjust their quality based on the acceptance threshold: workers who were above the threshold continued working at their usual quality level, and workers below the threshold self-selected themselves out of the task. Capitalizing on this consistency, we demonstrate that it is possible to predict workers' long-term quality using just a glimpse of their quality on the first five tasks.