Quality assessment in crowdsourced classification tasks

Purpose: Ensuring quality is one of the most significant challenges in microtask crowdsourcing. Aggregation of the collected data from the crowd is one of the important steps to infer the correct answer but the existing study seems to be limited to the single-step task. This study looks at multiple-step classification tasks and understands aggregation in such cases, hence is useful for assessing the classification quality. Design/methodology/approach: We present a model to capture the information of the workflow, questions, and answers for both single-question and multiple-question clas- sification tasks. We propose an adapted approach on top of the classic approach so that our model can handle tasks with several multiple-choice questions in general instead of a specific domain or any specific hierarchical classifications. We evaluate our approach with three representative tasks from existing citizen science projects in which we have the gold standard created by experts. Findings: The results show our approach can provide significant improvements to the overall classification accuracy. Our analysis also demonstrates that all algorithms can achieve higher accuracy for the volunteer- versus paid-generated datasets for the same task. Furthermore, we observed interesting patterns in the relationship between the performance of different algorithms and workflow specific factors including the number of steps, and the number of available options in each step. Originality/value: Due to the nature of crowdsourcing, aggregating the collected data is an important process to understand the quality of crowdsourcing results. Different inference algorithms have been studied for simple microtasks consisting of single questions with two or more answers. However, as classification tasks typically contain many questions, our proposed method is able to to be applied to a wide range of tasks including both single-question and multiple-question classification tasks.