[Paper Review] The blame game: Understanding blame assignment in social media

Ruijie Xi, Munindar P. Singh|arXiv (Cornell University)|Feb 26, 2023

Psychology of Moral and Emotional Judgment4 citations

TL;DR

This paper investigates blame assignment in social media using a dataset of 30,000 first-person moral narratives from the r/AmITheAsshole subreddit. It applies NLP to identify cognitive-affective language features and social factors—particularly gender and age—that influence audience judgments, revealing that males are more likely to be blamed regardless of role, and that language describing agentiveness significantly affects blame outcomes.

ABSTRACT

Cognitive and psychological studies on morality have proposed underlying linguistic and semantic factors. However, laboratory experiments in the philosophical literature often lack the nuances and complexity of real life. This paper examines how well the findings of these cognitive studies generalize to a corpus of over 30,000 narratives of tense social situations submitted to a popular social media forum. These narratives describe interpersonal moral situations or misgivings; other users judge from the post whether the author (protagonist) or the opposing side (antagonist) is morally culpable. Whereas previous work focuses on predicting the polarity of normative behaviors, we extend and apply natural language processing (NLP) techniques to understand the effects of descriptions of the people involved in these posts. We conduct extensive experiments to investigate the effect sizes of features to understand how they affect the assignment of blame on social media. Our findings show that aggregating psychology theories enables understanding real-life moral situations. Moreover, our results suggest that there exist biases in blame assignment on social media, such as males are more likely to receive blame no matter whether they are protagonists or antagonists.

Motivation & Objective

• To understand how linguistic and social features in real-life moral narratives influence blame assignment on social media.
• To assess whether psychological theories on morality generalize from lab settings to real-world online discourse.
• To identify specific language features—especially those tied to agentiveness and emotion—that affect audience perceptions of moral culpability.
• To investigate potential biases in blame assignment, particularly related to gender and age, in online moral judgment contexts.

Proposed method

• Proposes an entity-centric NLP framework to classify individuals in narratives as protagonist or antagonist.
• Operationalizes cognitive-affective language features from social psychology, including emotion-laden adjectives, moral foundation theory categories (e.g., care), and agentive language.
• Uses machine learning classifiers to predict blame verdicts based on linguistic and social features extracted from posts.
• Applies statistical modeling to measure effect sizes of features on blame assignment, enabling interpretability beyond prediction accuracy.
• Incorporates social factors such as gender and age of protagonists into the analysis to detect bias patterns.
• Validates findings using a large-scale corpus of 30,000 AITA posts with crowd-sourced blame verdicts.

Experimental results

Research questions

• RQ1 Which cognitive-affective language features most strongly influence blame assignment in social media narratives?
• RQ2 How do social factors such as gender and age affect blame assignment in real-life moral conflict posts?
• RQ3 To what extent do psychological theories of morality, such as the Theory of Dyadic Morality, generalize to real-world online discourse?
• RQ4 Are there detectable biases in blame assignment, particularly toward males, across different narrative roles?

Key findings

• Males are more likely to be blamed than females, regardless of whether they are protagonists or antagonists, indicating a systemic gender bias in online blame assignment.
• Posts describing individuals with fewer care-related words (from Moral Foundation Theory) are more likely to result in blame, suggesting that perceived concern for others reduces blame.
• Language features tied to agentiveness—such as active voice and intentional verbs—significantly increase the likelihood of blame assignment.
• The 15–45 age group shows heightened bias in blame assignment, particularly when males are protagonists, suggesting age-related moral typecasting.
• Psychological theories such as the Theory of Dyadic Morality generalize to real-world social media contexts, with linguistic cues of harm and vulnerability shaping blame outcomes.
• Machine learning models using psychology-grounded features achieve strong predictive performance, demonstrating the explanatory power of integrating social psychology into NLP for moral reasoning.