Towards A Reliable Ground-Truth For Biased Language Detection

TL;DR

This paper investigates the challenges of creating reliable ground-truth datasets for biased language detection, demonstrating that expert annotation significantly improves data quality over crowdsourcing.

Contribution

The authors develop a trained expert annotation framework that enhances the reliability of bias labels, leading to better bias detection system performance.

Findings

Expert labels have higher inter-annotator agreement than crowdsourced labels.

Training annotators improves data quality and bias detection accuracy.

A new dataset with improved reliability is created for bias detection research.

Abstract

Reference texts such as encyclopedias and news articles can manifest biased language when objective reporting is substituted by subjective writing. Existing methods to detect bias mostly rely on annotated data to train machine learning models. However, low annotator agreement and comparability is a substantial drawback in available media bias corpora. To evaluate data collection options, we collect and compare labels obtained from two popular crowdsourcing platforms. Our results demonstrate the existing crowdsourcing approaches' lack of data quality, underlining the need for a trained expert framework to gather a more reliable dataset. By creating such a framework and gathering a first dataset, we are able to improve Krippendorff's $\alpha$ = 0.144 (crowdsourcing labels) to $\alpha$ = 0.419 (expert labels). We conclude that detailed annotator training increases data quality, improving the performance of existing bias detection systems. We will continue to extend our dataset in the future.