# The Moral Foundations Reddit Corpus

arXiv: https://arxiv.org/abs/2208.05545 · HTML: https://mlsanity.com/paper/2208.05545/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 107 references that are in the map.
- First posted on arXiv 10 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://mlsanity.com/paper/1707.09457/) (2017)
- [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://mlsanity.com/paper/2110.05719/) (2021)
- [On Releasing Annotator-Level Labels and Information in Datasets](https://mlsanity.com/paper/2110.05699/) (2021)
- [Community Interaction and Conflict on the Web](https://mlsanity.com/paper/1803.03697/) (2018)
- [MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction](https://mlsanity.com/paper/1904.08314/) (2019)
- [The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments](https://mlsanity.com/paper/2203.14563/) (2022)
- [Hate Speech Classifiers Learn Human-Like Social Stereotypes](https://mlsanity.com/paper/2110.14839/) (2021)
- [Extracting Inter-community Conflicts in Reddit](https://mlsanity.com/paper/1808.04405/) (2018)
- [Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification](https://mlsanity.com/paper/2205.13139/) (2022)
- [Quantifying Latent Moral Foundations in Twitter Narratives: The Case of the Syrian White Helmets Misinformation](https://mlsanity.com/paper/2004.13142/) (2020)
- [Millions of Co-purchases and Reviews Reveal the Spread of Polarization and Lifestyle Politics across Online Markets](https://mlsanity.com/paper/2201.06556/) (2022)

## Built on by (most cited first, 21 of 21)

- [Knowledge of cultural moral norms in large language models](https://mlsanity.com/paper/2306.01857/) (2023)
- [From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models](https://mlsanity.com/paper/2308.12014/) (2023)
- [MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions](https://mlsanity.com/paper/2403.07678/) (2024)
- [A Data Fusion Framework for Multi-Domain Morality Learning](https://mlsanity.com/paper/2304.02144/) (2023)
- [Larger Language Models Don't Care How You Think: Why Chain-of-Thought Prompting Fails in Subjective Tasks](https://mlsanity.com/paper/2409.06173/) (2024)
- [MFTCXplain: A Multilingual Benchmark Dataset for Evaluating the Moral Reasoning of LLMs through Multi-hop Hate Speech Explanation](https://mlsanity.com/paper/2506.19073/) (2025)
- [Noise Audits Improve Moral Foundation Classification](https://mlsanity.com/paper/2210.07415/) (2022)
- [A Survey on Moral Foundation Theory and Pre-Trained Language Models: Current Advances and Challenges](https://mlsanity.com/paper/2409.13521/) (2024)
- [C-VARC: A Large-Scale Chinese Value Rule Corpus for Value Alignment of Large Language Models](https://mlsanity.com/paper/2506.01495/) (2025)
- [VALUEFLOW: Toward Pluralistic and Steerable Value-based Alignment in Large Language Models](https://mlsanity.com/paper/2602.03160/) (2026)
- [That's All Folks: a KG of Values as Commonsense Social Norms and Behaviors](https://mlsanity.com/paper/2303.00632/) (2023)
- [E2MoCase: A Dataset for Emotional, Event and Moral Observations in News Articles on High-impact Legal Cases](https://mlsanity.com/paper/2409.09001/) (2024)
- [Fairness Metric Design Exploration in Multi-Domain Moral Sentiment Classification using Transformer-Based Models](https://mlsanity.com/paper/2510.11222/) (2025)
- [LLMs Contain Multitudes: How Deployment Context Reshapes Model-Level Preferences and Values](https://mlsanity.com/paper/2606.13944/) (2026)
- [Explainable Moral Values: a neuro-symbolic approach to value classification](https://mlsanity.com/paper/2410.12631/) (2024)
- [Do Language Models Understand Morality? Towards a Robust Detection of Moral Content](https://mlsanity.com/paper/2406.04143/) (2024)
- [Generic Embedding-Based Lexicons for Transparent and Reproducible Text Scoring](https://mlsanity.com/paper/2411.00964/) (2024)
- [Semantic F1 Scores: Fair Evaluation Under Fuzzy Class Boundaries](https://mlsanity.com/paper/2509.21633/) (2025)
- [Reinforcing privacy reasoning in LLMs via normative simulacra from fiction](https://mlsanity.com/paper/2604.20904/) (2026)
- [Trusting sovereign language models as scientific instruments: evidence from Portugal's AMALIA](https://mlsanity.com/paper/2607.08731/) (2026)
- [Can Valence Reflect Morality in Natural Language? A Preliminary Annotation Study](https://mlsanity.com/paper/2607.20461/) (2026)

## Abstract (arXiv)

Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, environmental action, political engagement, and protest. Various computational methods in Natural Language Processing (NLP) have been used to detect moral sentiment from textual data, but achieving strong performance in such subjective tasks requires large, hand-annotated datasets. Previous corpora annotated for moral sentiment have proven valuable, and have generated new insights both within NLP and across the social sciences, but have been limited to Twitter. To facilitate improving our understanding of the role of moral rhetoric, we present the Moral Foundations Reddit Corpus, a collection of 16,123 English Reddit comments that have been curated from 12 distinct subreddits, hand-annotated by at least three trained annotators for 8 categories of moral sentiment (i.e., Care, Proportionality, Equality, Purity, Authority, Loyalty, Thin Morality, Implicit/Explicit Morality) based on the updated Moral Foundations Theory (MFT) framework. We evaluate baselines using large language models (Llama3-8B, Ministral-8B) in zero-shot, few-shot, and PEFT (Parameter-Efficient Fine-Tuning) settings, comparing their performance to fine-tuned encoder-only models like BERT (Bidirectional Encoder Representations from Transformers). The results show that LLMs continue to lag behind fine-tuned encoders on this subjective task, underscoring the ongoing need for human-annotated moral corpora for AI alignment evaluation. Keywords: moral sentiment annotation, moral values, moral foundations theory, multi-label text classification, large language models, benchmark dataset, evaluation and alignment resource