Demographic Biases of Crowd Workers in Key Opinion Leaders Finding
This addresses data quality issues for researchers and practitioners using crowdsourcing in social influence analysis, but it is incremental as it builds on existing bias mitigation techniques.
The paper tackles the problem of demographic biases in crowdsourced data for identifying Key Opinion Leaders (KOLs) by proposing a method to measure and mitigate these biases, resulting in a curated dataset with reduced bias.
Key Opinion Leaders (KOLs) are people that have a strong influence and their opinions are listened to by people when making important decisions. Crowdsourcing provides an efficient and cost-effective means to gather data for the KOL finding task. However, data collected through crowdsourcing is affected by the inherent demographic biases of crowd workers. To avoid such demographic biases, we need to measure how biased each crowd worker is. In this paper, we propose a simple yet effective approach based on demographic information of candidate KOLs and their counterfactual value. We argue that it is effectiveness because of the extra information that we can consider together with labeled data to curate a less biased dataset.