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Crowdsourcing has become a popular means to solicit assistance for scientific research. From classifying images or texts to responding to surveys, tapping into the knowledge of crowds to complete complex tasks has become a common strategy in social and information sciences. Although the timeliness and cost-effectiveness of crowdsourcing may provide desirable advantages to researchers, the data it generates may be of lower quality for some scientific purposes. The quality control mechanisms, if any, offered by common crowdsourcing platforms may not provide robust measures of data quality. This study explores whether research task participants may engage in motivated misreporting whereby participants tend to cut corners to reduce their workload while performing various scientific tasks online. We conducted an experiment with three common crowdsourcing tasks: answering surveys, coding images, and classifying online social media content. The experiment recruited workers from three sources: a crowdsourcing platform (Amazon Mechanical Turk) and a commercial online survey panel. The analysis seeks to address the following two questions: (1) whether online panelists or crowd workers may engage in motivated misreporting differently and (2) whether the patterns of misreporting vary by different task types. The study focuses on the analysis of the experiment in answering surveys and offers quality assurance practice guideline of using crowdsourcing for social science research.
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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.
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- Award ID(s):
- 1936968
- PAR ID:
- 10287980
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 5
- Issue:
- CSCW
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3479586
