Knowing Your Annotator: Rapidly Testing the Reliability of Affect Annotation

TL;DR

This paper introduces a real-time quality assurance tool for affect annotation tasks that predicts annotator reliability with 80% accuracy, improving the efficiency and validity of affective data collection.

Contribution

It presents a novel QA tool for rapid assessment of annotator reliability in audiovisual affect annotation tasks, enhancing data quality and reducing costs.

Findings

Trained annotators are more reliable than untrained crowdworkers.

The QA tool predicts annotator reliability with 80% accuracy.

The tool is accessible via the PAGAN annotation platform.

Abstract

The laborious and costly nature of affect annotation is a key detrimental factor for obtaining large scale corpora with valid and reliable affect labels. Motivated by the lack of tools that can effectively determine an annotator's reliability, this paper proposes general quality assurance (QA) tests for real-time continuous annotation tasks. Assuming that the annotation tasks rely on stimuli with audiovisual components, such as videos, we propose and evaluate two QA tests: a visual and an auditory QA test. We validate the QA tool across 20 annotators that are asked to go through the test followed by a lengthy task of annotating the engagement of gameplay videos. Our findings suggest that the proposed QA tool reveals, unsurprisingly, that trained annotators are more reliable than the best of untrained crowdworkers we could employ. Importantly, the QA tool introduced can predict effectively the reliability of an affect annotator with 80% accuracy, thereby, saving on resources, effort and cost, and maximizing the reliability of labels solicited in affective corpora. The introduced QA tool is available and accessible through the PAGAN annotation platform.