Morality plays an important role in social well-being, but people's moral perception is not stable and changes over time. Recent advances in natural language processing have shown that text is an effective medium for informing moral change, but no attempt has been made to quantify the origins of these changes. We present a novel unsupervised framework for tracing textual sources of moral change toward entities through time. We characterize moral change with probabilistic topical distributions and infer the source text that exerts prominent influence on the moral time course. We evaluate our framework on a diverse set of data ranging from social media to news articles. We show that our framework not only captures fine-grained human moral judgments, but also identifies coherent source topics of moral change triggered by historical events. We apply our methodology to analyze the news in the COVID-19 pandemic and demonstrate its utility in identifying sources of moral change in high-impact and real-time social events.

翻译：道德道德在社会福祉中起着重要作用,但人们的道德观念并不稳定,而且随时间而变化。自然语言处理的最近进展表明,文字是传播道德变革的有效媒介,但没有试图量化这些变革的起源。我们提出了一个新的、不受监督的框架,用于追踪随着时间的推移向实体提供道德变革的文字来源。我们用概率性主题分布来描述道德变革的特点,并推断对道德时间过程有显著影响的源文本。我们评估了我们关于从社会媒体到新闻文章等多种数据的框架。我们表明,我们的框架不仅抓住了精美的人类道德判断,而且还确定了历史事件引发的道德变革的连贯来源。我们运用我们的方法分析COVID-19大流行病中的新闻,并展示了它在确定影响大和实时社会事件道德变化的来源方面的有用性。