Morality plays a fundamental role in how we perceive information while
greatly influencing our decisions and judgements. Controversial topics,
including vaccination, abortion, racism, and sexuality, often elicit opinions
and attitudes that are not solely based on evidence but rather reflect moral
worldviews. Recent advances in natural language processing have demonstrated
that moral values can be gauged in human-generated textual content. Here, we
design a range of language representation models fine-tuned to capture exactly
the moral nuances in text, called MoralBERT. We leverage annotated moral data
from three distinct sources: Twitter, Reddit, and Facebook user-generated
content covering various socially relevant topics. This approach broadens
linguistic diversity and potentially enhances the models' ability to comprehend
morality in various contexts. We also explore a domain adaptation technique and
compare it to the standard fine-tuned BERT model, using two different
frameworks for moral prediction: single-label and multi-label. We compare
in-domain approaches with conventional models relying on lexicon-based
techniques, as well as a Machine Learning classifier with Word2Vec
representation. Our results showed that in-domain prediction models
significantly outperformed traditional models. While the single-label setting
reaches a higher accuracy than previously achieved for the task when using BERT
pretrained models. Experiments in an out-of-domain setting, instead, suggest
that further work is needed for existing domain adaptation techniques to
generalise between different social media platforms, especially for the
multi-label task. The investigations and outcomes from this study pave the way
for further exploration, enabling a more profound comprehension of moral
narratives about controversial social issues.

通过精心设计的 MoralBERT 模型，结合社交媒体平台的标注道德数据，探索了道德预测及领域适应技术在理解有争议的社会议题上的应用，结果表明在领域内的预测模型明显优于传统模型，但领域外的泛化仍需进一步探索。