Moral Sparks in Social Media Narratives

TL;DR

This work investigates how 'moral sparks' in social media narratives guide real-world moral judgments by analyzing r/AmITheAsshole posts. It integrates commonsense causal reasoning (via ATOMIC) with a rich set of linguistic features to identify quoted excerpts that trigger moral attention and influence blame decisions. The authors develop a multi-stage pipeline—extracting and aligning c-events, clustering, and computing Post and Cha linguistic features—and use logistic regression to link features to sparks and judgments. Key findings show negative trait cues amplify blame, sympathetic traits reduce blame, and linguistic signals (including moral foundations and power cues) modulate spark identification, offering empirical grounding for descriptive ethics and informing AI systems engaged in moral reasoning. The approach advances narrative understanding through causal reasoning on social media data and provides datasets and insights that could inform ethical AI design and the construction of morally grounded narrative resources.

Abstract

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.

Figures (9)

• Figure 1: Example post and a comment on it that exhibits a moral spark that in relevant to the commenter's judgment. NTA indicates the commenter stated that the author is not blameworthy.
• Figure 2: Example event-attribute relationships from ATOMIC hwang-2021-comet for the moral spark in Figure \ref{['fig:teaser']}.
• Figure 3: Our framework involves data collection, c-event selection, c-event clustering, linguistic feature extraction, and using regression models to analyze connections between moral sparks, c-events, linguistic features, and moral judgments.
• Figure 4: A COMPACTIE example. Italics are the descriptive adjectives that are part of the extracted triples.
• Figure 5: Human evaluation of (1) match between instances and c-events and (2) quality of names given to c-event clusters.