Quality Control in Crowdsourcing based on Fine-Grained Behavioral Features

Crowdsourcing is popular for large-scale data collection and labeling, but a major challenge is on detecting low-quality submissions. Recent studies have demonstrated that behavioral features of workers are highly correlated with data quality and can be useful in quality control. However, these studies primarily leveraged coarsely extracted behavioral features, and did not further explore quality control at the fine-grained level, i.e., the annotation unit level. In this paper, we investigate the feasibility and benefits of using fine-grained behavioral features, which are the behavioral features finely extracted from a worker's individual interactions with each single unit in a subtask, for quality control in crowdsourcing. We design and implement a framework named Fine-grained Behavior-based Quality Control (FBQC) that specifically extracts fine-grained behavioral features to provide three quality control mechanisms: (1) quality prediction for objective tasks, (2) suspicious behavior detection for subjective tasks, and (3) unsupervised worker categorization. Using the FBQC framework, we conduct two real-world crowdsourcing experiments and demonstrate that using fine-grained behavioral features is feasible and beneficial in all three quality control mechanisms. Our work provides clues and implications for helping job requesters or crowdsourcing platforms to further achieve better quality control.