一个追踪道德变化文本来源的无监督框架
An unsupervised framework for tracing textual sources of moral change
摘要：
道德在社会福祉中发挥着重要作用，但人们的道德感知并不稳定，并随着时间的推移而变化。自然语言处理的最新进展表明，文本是告知道德变化的有效媒介，但没有尝试量化这些变化的起源。我们提出了一个新的无监督的框架跟踪文本来源的道德变化对实体通过时间。我们用概率主题分布来描述道德变化，并推断出对道德时间进程产生显著影响的源文本。我们根据从社交媒体到新闻文章等多种数据来评估我们的框架。我们表明，我们的框架不仅捕捉细粒度的人类道德判断，但也确定了连贯的源主题的道德变革所引发的历史事件。我们运用我们的方法来分析COVID-19大流行的新闻，并展示其在识别高影响力和实时社会事件中道德变化来源方面的实用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
