Examining Temporal Bias in Abusive Language Detection
Mali Jin Yida Mu Diana Maynard Kalina Bontcheva
The use of abusive language online has become an increasingly pervasive
problem that damages both individuals and society, with effects ranging from
psychological harm right through to escalation to real-life violence and even
death. Machine learning models have been developed to automatically detect
abusive language, but these models can suffer from temporal bias, the
phenomenon in which topics, language use or social norms change over time. This
study aims to investigate the nature and impact of temporal bias in abusive
language detection across various languages and explore mitigation methods. We
evaluate the performance of models on abusive data sets from different time
periods. Our results demonstrate that temporal bias is a significant challenge
for abusive language detection, with models trained on historical data showing
a significant drop in performance over time. We also present an extensive
linguistic analysis of these abusive data sets from a diachronic perspective,
aiming to explore the reasons for language evolution and performance decline.
This study sheds light on the pervasive issue of temporal bias in abusive
language detection across languages, offering crucial insights into language
evolution and temporal bias mitigation.