Understanding moral values in social media text offers insight into moral judgement formation, and supervised NLP models trained on crowdsourced data have achieved strong classification performance. However, most approaches simplify the problem by aggregating multiple annotators’ labels into a single "ground truth", overlooking the inherent subjectivity of the task. In practice, there are disagreements between annotators caused by personal viewpoint or inherent ambiguities, particularly for short tweets. Here, we extend a pretrained language model with a layer that learns annotator-specific features. Our model improves predictions of individual annotations and yields representations that reveal meaningful insights into annotators’ moral perspectives. We show that models trained on aggregated labels may hide variation and give a misleading impression of performance. Overall, we demonstrate that disagreement reflects the inherent subjectivity of the task and that modelling individual perspectives creates benefits for moral classification of texts.

An increasing amount of historic data is now available in digital (text) formats. This gives quantitative researchers an opportunity to use distant reading techniques, as opposed to traditional close reading, in order to analyse larger quantities of historic data. Distant reading allows researchers to view overall patterns within the data and reduce researcher bias. One such data set that has recently been transcribed is a collection of over 500 Australian World War I (WW1) diaries held by the State Library of New South Wales. Here we apply distant reading techniques to this corpus to understand what soldiers wrote about and how they felt over the course of the war. Extracting dates accurately is important as it allows us to perform our analysis over time, however, it is very challenging due to the variety of date formats and abbreviations diarists use. But with that data, topic modelling and sentiment analysis can then be applied to show trends, for instance, that despite the horrors of war, Australians in WW1 primarily wrote about their everyday routines and experiences. Our results detail some of the challenges likely to be encountered by quantitative researchers intending to analyse historical texts, and provide some approaches to these issues.