No Time like the Present: Effects of Language Change on Automated Comment Moderation

The spread of online hate has become a significant problem for newspapers that host comment sections. As a result, there is growing interest in using machine learning (ML) and natural language processing (NLP) for (semi-) automated abusive language detection to avoid manual comment moderation costs or having to shut down comment sections altogether. However, much of the past work on abusive language detection assumes that classifiers operate in a static language environment, despite language and news being in a state of constant flux. In this paper, we show using a new German newspaper comments dataset that the classifiers trained with naive ML techniques like a random test-train split will underperform on future data, and that a time-stratified evaluation split is more appropriate. We also show that a classifier's performance rapidly degrades when evaluated on data from a different period than the training data. Our findings suggest that it is necessary to consider the temporal dynamics of language when developing an abusive language detection system or risk deploying a model that will quickly become defunct.