There is increasing interest in building computational models of moral reasoning by people to enable effective interaction by Artificial Intelligence (AI) agents. We examine interactions on social media to understand human moral judgments in real-life ethical scenarios. Specifically, we examine posts from a popular Reddit subreddit (i.e., a subcommunity) called r/AmITheAsshole, where authors and commenters share their moral judgments on who (i.e., which participant of the described scenario) is blameworthy. To investigate the underlying reasoning influencing moral judgments, we focus on excerpts-which we term moral sparks-from original posts that some commenters include to indicate what motivates their judgments. To this end, we examine how (1) events activating social commonsense and (2) linguistic signals affect the identified moral sparks and their subsequent judgments. By examining over 24672 posts and 175988 comments, we find that event-related negative character traits (e.g., immature and rude) attract attention and stimulate blame, implying a dependent relationship between character traits and moral values. Specifically, we focus on causal graphs involving events (c-events) that activate social commonsense. We observe that c-events are perceived with varying levels of informativeness, influencing moral spark and judgment assignment in distinct ways. This observation is reinforced by examining linguistic features describing semantically similar c-events. Moreover, language influencing commenters' cognitive processes enhances the probability of an excerpt becoming a moral spark, while factual and concrete descriptions tend to inhibit this effect.

翻译：人们越来越关注构建人类道德推理的计算模型，以推动人工智能（AI）代理实现有效交互。我们通过分析社交媒体上的互动，探究现实伦理情境中的人类道德判断。具体而言，我们研究了知名Reddit子社区r/AmITheAsshole（即AITA子版块）中的帖子，其中发帖者和评论者就谁（即所描述情境中的哪一方参与者）应受谴责分享了各自的道德判断。为揭示影响道德判断的潜在推理过程，我们聚焦于原始帖子中的摘录片段（称之为“道德火花”），这些片段被部分评论者引用以表明其判断动机。为此，我们考察了（1）激活社会常识的事件以及（2）语言信号如何影响被识别的道德火花及其后续判断。通过对24672篇帖子和175988条评论的分析，我们发现与事件相关的负面性格特征（如幼稚与粗鲁）会吸引注意力并激发谴责，暗示性格特征与道德价值观之间存在依赖关系。具体而言，我们聚焦于激活社会常识的事件因果关系图（c-events）。观察到c-events的信息量感知存在差异，从而以不同方式影响道德火花与判断的分配。这一发现通过分析描述语义相似c-events的语言特征得到进一步证实。此外，影响评论者认知过程的语言能提高摘录片段成为道德火花的概率，而事实性与具体性描述则倾向于抑制这种效应。