[Paper Review] Explainable Patterns for Distinction and Prediction of Moral Judgement on Reddit

Ion Stagkos Efstathiadis, Guilherme Paulino-Passos+1 more|arXiv (Cornell University)|Jan 26, 2022

Hate Speech and Cyberbullying Detection4 citations

TL;DR

This paper introduces a new dataset of 600K Reddit comments from r/AmITheAsshole and proposes BERT-based classifiers to predict moral judgments in both posts (62% accuracy) and comments (86% accuracy), identifying explainable textual patterns via GrASP that link language to moral stances and model decisions, enhancing interpretability and trust in AI-driven moral judgment prediction on social media.

ABSTRACT

The forum r/AmITheAsshole in Reddit hosts discussion on moral issues based on concrete narratives presented by users. Existing analysis of the forum focuses on its comments, and does not make the underlying data publicly available. In this paper we build a new dataset of comments and also investigate the classification of the posts in the forum. Further, we identify textual patterns associated with the provocation of moral judgement by posts, with the expression of moral stance in comments, and with the decisions of trained classifiers of posts and comments.

Motivation & Objective

• To address the challenge of predicting moral judgments in social media, particularly in unstructured, subjective, and long-form narratives from Reddit.
• To construct a publicly available dataset of 600K labeled Reddit comments from r/AmITheAsshole to support reproducible research in moral judgment classification.
• To improve interpretability of moral judgment classifiers by identifying textual patterns associated with moral stances and model predictions using GrASP.
• To investigate the limitations of BERT in classifying posts due to long context, class imbalance, and subjective labeling, and to propose improvements.

Proposed method

• Fine-tuned BERT-base-cased model for binary classification of posts and comments into 'asshole' (YTA/ESH) and 'not asshole' (NTA/NAH) verdicts.
• Constructed a new dataset of 600K primary comments from r/AmITheAsshole by scraping via Pushshift API, filtering for quality and verdict tags, and removing tags to prevent leakage.
• Applied GrASP (Global Rule-based Pattern Search) to extract high-information-gain textual patterns linking comments/posts to actual labels and BERT-predicted labels.
• Used information gain to rank patterns, enabling identification of language cues associated with moral stance expression and classifier decisions.
• Conducted hyperparameter tuning and evaluated performance using accuracy, precision, recall, and F1 scores on unbalanced test sets.
• Compared BERT performance with baseline models (e.g., Multinomial Naive Bayes, Logistic Regression) and analyzed discrepancies due to class imbalance and model bias.

Experimental results

Research questions

• RQ1 What is the performance of BERT in classifying moral judgments in Reddit posts, and what challenges limit its accuracy?
• RQ2 How do textual patterns correlate with actual moral stances and BERT-predicted labels in comments and posts, and what sociological insights do they provide?
• RQ3 To what extent do explainable patterns extracted via GrASP improve transparency and trust in moral judgment classifiers on social media?
• RQ4 Why does the classifier perform significantly worse on posts than on comments, and what factors contribute to this gap?
• RQ5 Can patterns from BERT-predicted labels reveal model biases or inconsistencies not evident in actual labels?

Key findings

• The BERT classifier achieved 62% accuracy in predicting moral judgments for Reddit posts, failing to outperform the majority-class baseline, highlighting the task's difficulty due to long texts, subjectivity, and class imbalance.
• For comments, the BERT classifier achieved 86% accuracy, outperforming the majority-class baseline, indicating that comment-level moral judgments are more predictable than post-level ones.
• GrASP identified high-information-gain textual patterns that associate specific language with moral stances, such as 'I just' or 'I didn’t realize' being linked to 'sweetheart' (NTA/NAH) verdicts.
• Patterns derived from BERT-predicted labels showed significantly higher information gain than those from actual labels, suggesting that model decisions are more consistent than human-labeled data.
• The word 'edit' was misclassified by BERT as indicative of 'sweetheart' despite being more often associated with 'asshole' in practice, revealing a limitation in BERT’s contextual understanding of neutral words.
• Manual correction of GrASP patterns revealed that some patterns initially linked to 'sweetheart' were actually indicative of 'asshole', underscoring the need for human-in-the-loop validation of explainability tools.