Modeling Human Annotation Errors to Design Bias-Aware Systems for Social Stream Processing
This work addresses annotation bias for social media analytics, but it is incremental as it builds on existing active learning methods with a focus on scheduling.
The paper tackles the problem of low-quality human annotations in social media machine learning by showing that annotation quality depends on the ordering of instances, and proposes an error-mitigating active learning algorithm that increases accuracy and bias awareness in classification tasks during crises.
High-quality human annotations are necessary to create effective machine learning systems for social media. Low-quality human annotations indirectly contribute to the creation of inaccurate or biased learning systems. We show that human annotation quality is dependent on the ordering of instances shown to annotators (referred as 'annotation schedule'), and can be improved by local changes in the instance ordering provided to the annotators, yielding a more accurate annotation of the data stream for efficient real-time social media analytics. We propose an error-mitigating active learning algorithm that is robust with respect to some cases of human errors when deciding an annotation schedule. We validate the human error model and evaluate the proposed algorithm against strong baselines by experimenting on classification tasks of relevant social media posts during crises. According to these experiments, considering the order in which data instances are presented to human annotators leads to both an increase in accuracy for machine learning and awareness toward some potential biases in human learning that may affect the automated classifier.