Morality is a foundational lens for understanding behavior on social media, shaping how individuals evaluate conflicts, assign blame, and navigate interpersonal dilemmas in digital environments [[[11]]]. Online interactions are often governed by implicit moral frameworks that reflect collective beliefs about right and wrong, fairness, and harm. Prior work has highlighted the need to address harmful behaviors that violate ethical expectations and erode trust in online communities [[[10]]], revealing how different platforms surface divergent value systems [[[11]]], and exhibiting ideological biases, particularly in relation to politicians’ viewpoints [[[21]]]. These studies underscore the importance of moral reasoning in promoting safety and inclusivity online, yet few directly engage with morality as a primary analytical lens.

Figure 1: Example post and a comment on it from AITA, exhibiting a moral spark that is relevant to the commenter’s judgment. NTA indicates the commenter stated that the author is not blameworthy.

Recently, a growing body of computational research is exploring the moral dimensions of online platforms, which offer a rich, real-world source of moral dilemmas across diverse contexts [[[11]], [[33]], [[39]], [[51]], [[52]], [[56]]]. Online platforms like r/AmITheAsshole (AITA) provide an ideal environment for computational social research, enabling the study and application of moral reasoning that mirrors human morality. Figure 1 illustrates an example of an AITA post and its corresponding comments. In AITA, users describe interpersonal conflicts and seek judgments on whether they are at fault (YTA) or not (NTA). Commenters provide their verdicts along with reasons for their judgments, sometimes highlighting excerpts. We term these excerpts as moral sparks. Since they indicate moral attention that cuts through extraneous information [[[41]]], which are common in moral narratives. Despite their importance in guiding moral judgments, moral sparks have yet to be systematically studied, this paper fills this gap.

Despite growing interest in AITA, prior research has largely overlooked the moral reasoning mechanisms that underpin user judgments—missing a critical opportunity to interpret online behavior through the lens of ethical theory. For example, Affective Disposition Theory (ADT) [[[59]]] posits that individuals form moral judgments and emotional responses based on a character’s traits and dispositions, aligning positively or negatively depending on perceived moral valence. Recent extensions [[[35]], [[49]]] further suggest that moral judgments are shaped not only by observable actions but also by inferred motivations. Computational studies have shown that these principles are applicable to AITA, with linguistic features like anger serving to amplify perceived blame [[[11]], [[17]], [[56]]]. Moreover, even subtle changes in linguistics may result in differing moral judgments describing the same situation [[[51]]]. Despite these advancements, no research has explored whether moral attention, as reflected by moral sparks, practically mirrors the effects proposed by ADT.

Figure 2: An example of event-attribute relationships from ATOMIC [[[25]]] for the moral spark in Figure 1.

Moreover, social commonsense reasoning subtly shapes the interpretation of character traits by leveraging contextual knowledge, even when such traits are not explicitly stated in the text [[[2]]]. To formalize this, we define a commonsense event (c-event) by integrating ATOMIC, a causal event graph encompassing nine relation types [[[25]]]. Our focus is on the xAttr relation, which captures the perceived attributes of participants (e.g., “PersonX forces” implies that PersonX is controlling, aggressive, or powerful). As illustrated in Figure 2, identifying an event like forces highlights controlling traits, demonstrating how linguistic cues inform a character. These c-events shape readers’ understanding of moral narratives, directing their attention and influencing their judgments.

Previous works have applied normative reasoning to Large Language Models (LLMs) to predict moral judgments [[[22]], [[27]], [[33]]]. However, these models rely on reasoning derived from AITA post titles, extended through generative methods or human annotations [[[14]], [[16]], [[33]], [[57]], [[58]]]. Norms derived solely from titles fail to capture the moral complexity of AITA narratives. For example, the title “AITA for raging at my aunt because she strongly believes I should eat meat”, omits critical details such as repeated boundary violations (illustrated by the moral spark in Figure 1) or a single misunderstanding that could significantly alter the moral conflict. These missing elements are crucial for understanding the situation and the moral judgments it elicits.

Methods. To address these research gaps, we develop a comprehensive methodology to study AITA narratives, focusing on moral sparks. To validate that the quotes function as moral cues and drive moral judgments in comments, we conduct a human evaluation, as outlined in Section 4. We identify verb-driven event triples from moral sparks using the COMPACTIE system [[[15]]]. We then examine how c-events, along with linguistic features like emotions, contribute to the selection of an excerpt as a moral spark through logistic regression analysis. We use transfer learning to determine if incorporating moral sparks improves the performance of moral judgment classification. Finally, we analyze model predictions through rationalization, evaluating whether moral sparks serve as anchors for contextualizing moral judgments.

Findings. We find that Affective Disposition Theory (ADT) [[[59]]] applies to scenarios on social media. For example, negative character traits, such as rude, amplify moral attention, whereas sympathetic traits like vulnerable attenuate it. Linguistic features, framed by Moral Foundation Theory [[[20]]], highlight the impact of emotionally charged terms (e.g., cheating) in increasing moral attention, whereas positive and neutral terms (e.g., leisure and family members) diminish it. Moreover, our experiments with sequential transfer learning [[[10]], [[32]], [[44]]] and soft rationalization [[[26]], [[52]]] show that integrating moral sparks enhances model performance in moral judgment classification. Rationales for these classifications often overlap with the c-events that shape the likelihood of a moral spark’s appearance. For example, the c-event can’t help aligns with rationales like rude and selfish, triggering moral sparks, whereas positive traits such as loyalty and distressed correlate with will always love and would be upset, reducing the likelihood of moral sparks’ appearance. These results underscore how moral sparks anchor situational narratives, shaping and reinforcing moral judgments.

Affective Disposition Theory (ADT) [[[59]]] explains how character traits influence moral judgment and emotional responses. ADT has been applied to drama [[[59]]], to explore why certain narratives engage audiences. Tamborini et al. [[[49]]] show that ADT’s effectiveness in understanding morally complex characters depends on narrative cues, whereas Matthews and Bonus [[[35]]] find that characters’ behaviors can dynamically shift audience moral judgments.

Social media narrative analysis employs methods like word embeddings, topic modeling [[[40]]], and sentiment analysis [[[51]]]. These narratives are challenges, as authors often act as both narrator and character, subtly shaping perceptions through language [[[45]]]. Linguistic features such as passive voice and power dynamics further influence interpretation [[[56]]]. Lourie et al. [[[33]]] used AITA-derived social norms to predict moral judgments. Statistical analyses of AITA posts reveal moral judgment patterns, including biases related to gender and tone [[[4]], [[11]], [[51]]]. Reasoning-aware hypertext study is crucial for interpreting nuanced opinions, combating misinformation, and promoting ethical AI systems [[[19]], [[54]]]. Social commonsense has been applied to identify event causality in social media, with datasets on moral actions and everyday rules aiding cultural bias detection and improving language model performance [[[2]], [[14]]].

Figure 3: Our framework, where M-sparks represents moral sparks.

This section outlines the data and methods employed in this study. Figure 3 shows our framework. We start from extracting moral sparks from these narratives. Then, we align moral sparks with ATOMIC [[[25]]], label the sparks with two processes, and extract linguistic features for further analysis.

3.1 Collect Moral Sparks

3.2 Collect C-Events

3.3 Label Moral Sparks

3.4 Extract Linguistic Features

Linguistic signals drawn from cognitive science capture nuanced information in AITA moral narratives [[[51]], [[56]]]. Building on Xi and Singh [[[51]]] and Giorgi et al. [[[17]]], we construct a feature vector for each instance, integrating Post (context-level) and Cha (character-level) features, described in detail below:

Post: Moral Content (MFT)Moral Foundation Theory (MFT) [[[20]]], a widely used in analysis on Reddit [[[39]], [[51]]], proposes that human moral reasoning is based on five core foundations: care/harm, fairness/cheating, loyalty/betrayal, authority/subversion, and sanctity/degradation. We use the extended Moral Foundations Dictionary (eMFD) [[[23]]], containing 2,041 words across five MFT domains, each with a composite valence score [ − 1, 1].

Post: NRC VADValence, Arousal, and Dominance (VAD) scores [[[37]]] is a lexicon of 20,000 words scored in [0, 1] that captures affective tone in text.

Post: NRC EmotionUsing Mohammad and Turney’s ([[38]]) NRC Emotion lexicon, we represent 10,170 words with eight basic emotions from the emotion model of: anger, fear, anticipation, trust, surprise, sadness, joy, and disgust [[[43]]], yielding an eight-dimensional vector for each instance.

Post: LIWCLIWC [[[42]]] organizes words into social and psychological categories (e.g., Lifestyle). We use the expanded LIWC-22 dictionary [[[5]]] to vectorize word counts per instance, previously applied to measure real-life moral scenarios [[[51]]].

Post: SubjectivitySubjectivity scores are computed per post from a lexicon with values based on word strength and polarity: 0.5 for weaksubj and 1 for strongsubj, adjusted by polarity [[[50]]].

Post: SentimentVADER [[[24]]] determines nominal sentiment per instance: negative (below − 0.05), positive (above 0.05), or neutral (in between).

Cha: Connotation FramesWe adopt 1,000 common verbs from a lexicon includes four connotative dimensions [ − 1, 1] for subject and object: effects, values, mental states, and authors’ perspectives [[[45]]].

Cha: Power and AgencyThe power and agency dataset [[[47]]] extends connotation frames, assigning implicit power and agency levels to 1,737 power verbs and 2,146 agency verbs. The lexicon delineates power dynamics; for instance, in “X fears Y,”, Y holds power, whereas in “X pushes Y,”, X wields power. The author’s power score increases when they are the subject in a power shift from subject to object, and reduces if they are the object. Agency scores are computed similarly. Characters with power or agency over others are marked 1, and others are marked 0.

Although some lexicons, such as MFT [[[23]]], provide scores for individual words, we follow prior work [[[51]]] by counting the occurrences of lexicon terms normalized by the number of extracted event triples. Scores are inverted when negative edges (e.g., "not") are present in the dependency structure. For Post:sentiment, we compute the average of compound sentiment scores. The persona sets used to collect Cha features align with those detailed in Section 3.2. Cha scores are normalized based on the number of descriptive words (e.g., ADJ, ADV) associated with characters (i.e., Author and Others persona sets) in the dependency tree.

Human evaluation of (1) match between instances and c-events and (2) quality of names given to c-event clusters.

Figure 4: Human evaluation of (1) match between instances and c-events and (2) quality of names given to c-event clusters.

An OR > 1 indicates that the given c-event or linguistic feature triggers an instance becoming a moral spark and OR < 1 indicates the opposite. All features have p < 0.05. C-events are labeled as “Event (example xAttr),” and linguistic features are labeled “Feature [Lexicon] (example words).” CF denotes Connotation Frame.

Figure 5: An OR > 1 indicates that the given c-event or linguistic feature triggers an instance becoming a moral spark and OR < 1 indicates the opposite. All features have p < 0.05. C-events are labeled as “Event (example xAttr),” and linguistic features are labeled “Feature [Lexicon] (example words).” CF denotes Connotation Frame.

We designed two surveys to evaluate (1) whether moral sparks function as moral cues and drive moral judgments in comments, and (2) whether each instance and its matched c-events make sense to people. We randomly selected 300 instances with their candidates and asked four independent raters (referred to as R1, R2, R3, and R4) to provide ratings on a five-point Likert scale. The survey questions for Survey 1 is: How likely is a moral spark is related to triggering the commenter’s moral judgment about the post? The survey question for Survey 2 is: How closely does a c-event describe what happened in an instance? Both surveys require raters to provide a rating from 1 to 5, with 1 being not related and 5 being extremely related.

Figure 4a presents the survey results. The violin plots illustrate the rating distributions provided by the four raters for each survey. For Survey 1, the average rating is above “strongly related” (μ = 4.16), with R1 providing the highest average rating (μ = 4.35) and R3 providing the lowest (μ = 3.89). The heatmap displays the inter-rater agreements of moderate general agreement among the raters (r = 0.535). For Survey 2, the ratings similarly averaged above “strongly related” (μ = 4.18), with moderate agreement between the ratings (r = 0.516). Collectively, these results support the validity of our methodology for extracting and aligning moral sparks and its function on driving comments to give judgments, and social media data with commonsense knowledge, as perceived by human evaluators.

We employ a logistic regression model to analyze the relationship between features (i.e., commonsense and linguistic features) that trigger or attenuate moral attention, specifically in determining whether an instance qualifies as a moral spark. We recognize that even subtle changes in linguistics may result in differing moral judgments of the same situation [[[51]]]. However, investigating the effects on individuals is beyond the scope of this study and will be addressed in future work. Though logistic regression can capture associations between features and tasks [[[11]], [[28]], [[36]]], it suffers from some limitations. In particular, features such as social commonsense may introduce multicollinearity, potentially distorting regression estimates and complicating the interpretability of results. Additionally, logistic regression assumes a linear relationship between features and log odds, overlooking nonlinear dynamics and hierarchical relationships, which could affect accuracy. Despite these challenges, we proceed with this method due to its established success. The model is defined as:$(1)(begin{equation}log frac{P(Y=1)}{1-P(Y=1)} = beta 0 + beta X X + sum {i=1}^{n}alpha i Diend{equation})$ where X represents the feature values, D~i~ indicates the presence of a specific feature (e.g., c-event or linguistic), and Y is the binary label of whether an instance is a moral spark. β~X~ represents the regression coefficient, and exp (β~X~) is the odds ratio (OR), interpreting the change in odds for a one-unit increase in X. To ensure reliability, we apply a False Discovery Rate (FDR) correction [[[3]]] and test whether the p-value for β~X~ is below 0.05.

Figure 5a and Figure 5b show that certain c-events, such as “threatens someone’s existence” (OR = 3.33), strongly correlate with moral attention, suggesting that aggressive or harmful actions are more likely to get a reader’s focus. Conversely, c-events associated with positive character traits, such as “will always love someone” (OR = 0.31), are less likely to trigger moral attention. Notably, the xAttr attribute of c-events shows similar effects to linguistic features, with attributes like loyal reducing moral attention, paralleling the influence of “loyalty” observed in the MFT framework [[[20]]].

Moreover, Figure 5c and Figure 5d illustrate that linguistic features tied to negative emotions and MFT—like abandoned (under anger)—as amplifiers of moral attention. However, concrete and positive terms (e.g., “bio” and “leisure”) weaken moral attention generally. A comparison with previous findings [[[51]]] reveals consistent patterns: linguistic triggers like anger, dominance, and agentive actions amplify perceptions of blameworthiness, whereas positive attributes such as loyalty weaken these effects in both moral attention and judgments. However, we observe discrepancies with other features, such as anticipation, which show divergent effects across moral attention and moral judgments. These findings highlight the complex interplay between linguistic features and moral judgments in real-world applications.

We conduct transfer learning experiments to investigate the correlation between moral sparks and moral judgments. Unlike previous work that generates social norms through human-intensive annotation or generative LLMs [[[14]], [[16]], [[33]], [[57]], [[58]], [[58]]], we use moral sparks normative reasoning. Moreover, we perform rationalization proxies for examining the reasons behind a model’s decision after learning from moral sparks when classifying moral judgments. Transfer learning enables assessing whether moral sparks contribute to aligning model predictions with human-like reasoning, whereas rationalization provides insights into their contributions to moral judgments.

6.1 Transfer Learning

6.2 Rationalization for Identifying Moral Judgments

We employ a rationalization process [[[1]], [[13]], [[30]]] to identify the key factors influencing moral judgment verdicts. Rationales are concise, sufficient segments of the input text that highlight the most critical information influencing the model’s decision [[[26]]]. Given a pretrained model $(mathcal {M})$, each instance is represented as (x, y), where x = [x^i^] denotes the input tokens and y ∈ 0, 1 denotes the binary label (e.g., blameworthy or not). The rationalization process outputs a predicted label $(hat{y})$ and a binary mask z = [z^i^] ∈ 0, 1, where z^i^ = 1 indicates that the i-th token is used in the decision. The tokens identified by this mask are the rationales used to explain the model’s prediction.

Table 6: Metrics for evaluating rationales, as described by Chrysostomou and Aletras [[[7]]]. Each ranges from 0 to 1, with higher values indicating greater importance of the rationales.

Metric	Description	Value
Macro-F1 Drops	Loss in F1 for predicting with rationale-reduced input compared to full input	0.576
Normalized Sufficiency (NS)	Reversed and normacen predicting full text and rationales	0.539
Normalized Comprehensiveness (NC)	Normalized prediction differences between full text and rationale-reduced text	0.681

Important tokens identified by RoBERTa in predicting moral judgment for MS-Target.

Figure 6: Important tokens identified by RoBERTa in predicting moral judgment for MS-Target.

We employ soft selection [[[26]]], where is well-suited for scenarios without ground-truth rationales and avoids the limitations of hard selection [[[13]]]. We calculate importance scores z through attention weights multiplied by gradients [[[48]]]. We collect rationales from the best-performing model for MS-Target (as shown in the penultimate row of Table 5), the RoBERTa model with Aggregate. To evaluate whether these rationales effectively capture the contextual information most relevant to the model’s predictions, we compute the metrics detailed in Table 6. The results confirm that the extracted rationales are of moderate to high quality [[[7]]], indicating their reliability.

Figure 6 illustrates the identified rationales. A notable overlap exists between the rationales and c-events, including their XAttr, which collectively shape moral attention, as illustrated in Figure 5. For instance, the c-event “can’t help” aligns with rationales such as “rude” and “selfish,” whereas “calling someone a liar” corresponds to “lied,” and “threatening someone’s existence” aligns with “threats.” Positive attributes also exhibit alignment, such as “loyalty” correlating with “will always love” and “distressed” matching “would be upset.” These alignments underscore the importance of moral sparks in anchoring the reasoning for moral judgments by directing the reader’s attention through narrative cues.

Our research studies how language models benefit from modeling moral reasoning by analyzing real-world data from the subreddit r/AmITheAsshole (AITA). We investigate the identification of and the ways in which models leverage them to enhance understanding of moral decision-making. This work underscores the importance of moral sparks in shaping moral judgments and contributes to computational social research on understanding human values on social media.

7.1 Findings

7.2 Limitations and Threats to Validity

We thank the anonymous reviewers for their helpful comments. We thank the NSF (grant IIS-2116751) for partial support for this research.

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