Moral Foundations Dictionary (MFD) Overview

• The Moral Foundations Dictionary is a lexicon that maps English words and word-stems to five primary moral foundations, such as Care/Harm and Fairness/Cheating.
• It has evolved through expansions like eMFD, MoralStrength, and J-MFD, incorporating probabilistic assignments and multilingual adaptations for greater vocabulary coverage.
• Integration with n-gram models, topic modeling, and pre-trained language models highlights its impact on social media analysis, moral rhetoric evaluation, and computational moral psychology.

The Moral Foundations Dictionary (MFD) is a lexicon created to operationalize the principles of Moral Foundations Theory (MFT) within computational linguistic research. The MFD maps English words and word-stems to five core moral foundations—Care/Harm, Fairness/Cheating, Loyalty/Betrayal, Authority/Subversion, and Sanctity/Degradation—providing a basis for quantifying references to moral intuitions in textual corpora. Subsequent expansions and adaptations (e.g., eMFD, MoralStrength, LibertyMFD, J-MFD) address its limited vocabulary and enable its use across languages, platforms, and analytic pipelines. The MFD, frequently integrated with n-gram, topic modeling, and embedding-based approaches, serves as a benchmark in moral rhetoric analysis, social-media monitoring, and evaluation of pre-trained LLMs.

1. Theoretical Foundations and Lexicon Structure

Moral Foundations Theory (MFT), as articulated by Haidt, Graham, Joseph, and collaborators, posits that human moral reasoning is undergirded by evolutionarily-conserved, semi-independent “foundations”: Care/Harm, Fairness/Cheating, Loyalty/Betrayal, Authority/Subversion, and Sanctity/Degradation. Each is manifest in language both through “virtue” (positive) and “vice” (negative) forms (Zangari et al., 20 Sep 2024, Stanier et al., 29 Jun 2024).

The original MFD (MFDv1; Graham et al. 2009) contains 324 English word stems and full forms, with an average of 32 items per moral category. Terms are manually mapped to one or more foundation/polarity pairs and were selected from Moral Foundations Questionnaire (MFQ) items, English thesauri, and expert vetting for contextual relevance (Zangari et al., 20 Sep 2024). Table 1 illustrates prototype MFD entries:

Foundation	Example Virtue Terms	Example Vice Terms
Care/Harm	care, protect	hurt, harm, kill
Fairness/Cheating	fair, just	cheat, bias
Loyalty/Betrayal	loyal, patriot	traitor, betray
Authority/Subversion	obey, respect	rebel, defy
Sanctity/Degradation	pure, virtuous	sin, defile

Sources: (Stanier et al., 29 Jun 2024, Zangari et al., 20 Sep 2024, Araque et al., 2019)

2. Expansion and Cross-Linguistic Adaptation

To address the MFD’s coverage limitations and adapt it for broader analyses, several expanded and multilingual versions have been developed:

• MFDv2: Expands to 2,014 single-word entries, mapped to one foundation each (Zangari et al., 20 Sep 2024).
• eMFD (Hopp et al. 2021): 3,270 unique English lemmas with probabilistic foundation assignments $p_f(w)$ and virtue/vice sentiment scores $s_f(w)$. Annotation is via crowd-sourced judgments on news sentences, yielding fine-grained, context-led categories (Gamage et al., 2023).
• MoralStrength: Derived using WordNet synset expansion, yielding ~996 lemmas per (Araque et al., 2019). Each lemma receives a continuous “moral valence” $r_{\ell,f}$ from crowdsourced ratings, providing granular weighting (mean valence $\bar r_f =$ 5.9–6.8 across foundations).
• LibertyMFD: Introduces the Liberty/Oppression foundation via corpus-driven induction from contrasting news sources, using compositional semantics and word-embedding similarity to assign polarity scores to $\sim$15,000 lemmas (Araque et al., 2022).
• J-MFD: Japanese adaptation created through semi-automated translation, corpus frequency filtering, and manual vetting, containing 741 validated entries (Matsuo et al., 2018).

Extensions maintain separate “virtue”/“vice” categorizations where possible and typically validate coverage and construct validity through word-frequency–rank selection or associations with MFQ self-reports (Matsuo et al., 2018).

3. Methodologies for Operationalization in Text Analysis

The MFD and its descendants are utilized in diverse text analysis workflows:

• Frequency-based moral scoring: For corpus $D$, compute foundation-specific frequencies as $f_f = (\sum_{w \in M_f} \text{count}(w)) / N_{tot}$ (Stanier et al., 29 Jun 2024). Weighted versions use $f(w)$ or term weights (e.g., tf-idf): $$\mathrm{Score}_f(D) = (1/N) \sum_{w \in D} \text{tf}(w)\cdot f(w)$$ (Zangari et al., 20 Sep 2024).
• N-gram and collocation modeling: Extraction of MFD word–containing bigrams/trigrams reveals characteristic foundations and rhetorical strategies (e.g., “kill child” in r/prolife, “forced birth” in r/prochoice) (Stanier et al., 29 Jun 2024).
• Topic modeling: Latent Dirichlet Allocation (LDA) is used to identify topics structured by MFD-word presence; moral terms often serve as discriminant features for topic labeling (e.g., “value of life,” “rights of mothers”) (Stanier et al., 29 Jun 2024).
• Embedding-based generalization: “Vec-tionary” approaches optimize foundation axes in embedding space, leveraging eMFD seeds: for word vector $\mathbf w_i$, relevance is $s_i = p_i \times v_i$; optimal axis $\mathbf m$ minimizes $$L(\mathbf m) = \sum_{i=1}^N (\mathbf w_i \cdot \mathbf m - s_i)^2$$ with $\lVert \mathbf m \rVert = 1$ (Duan et al., 2023). This yields continuous, context-sensitive scoring.
• Binary and weighted lexicon lookup: Classifiers using MFD-based features achieve F$_1$ scores of 0.28–0.40 per dimension; augmented or embedding-informed workflows can yield macro-F$_1$ in the mid-80s (Araque et al., 2019).

4. Validation, Reliability, and Comparative Evaluation

Multiple validation strategies are deployed:

• Crowd and expert annotation: eMFD and MoralStrength employ large-scale human judgment to establish word–foundation mappings and assign valence/strength scores. Inter-annotator agreements (Cohen’s κ, Gwet’s AC$_2$) are generally in the moderate to high range (0.42–0.92 depending on foundation and method) (Araque et al., 2019, Gamage et al., 2023).
• Correlational analyses: Frequency of foundation-matched language is tested for association with self-reported MFQ scores, particularly robust for Harm and Fairness (Matsuo et al., 2018).
• Supervised classification benchmarks: Both dictionary-based features and embedding-derived features are pitted against unigrams and state-of-the-art systems. Embedding-informed methods (e.g., SIMON, vec-tionary) systematically outperform raw MFD counts (Duan et al., 2023, Araque et al., 2019).
• Construct validity: Cluster and topic analyses reveal congruence between inferred foundation distributions and known group stances (e.g., abortion debate stances aligning with Care/Harm vs. Authority language) (Stanier et al., 29 Jun 2024).

5. Known Limitations and Best Practices

Identified limitations of the MFD and its derivatives include:

• Vocabulary coverage: Static lexicons quickly become outdated, missing new coinages and domains (especially in fast-evolving social media) (Zangari et al., 20 Sep 2024).
• Contextual ambiguity and (de)negation: Lexicon-based matching confounds with negation (“not loyal”) and sarcasm; best practice is to combine with syntactic or rule-based screening if possible (Mutlu et al., 2020).
• Cross-linguistic/cultural bias: Nearly all MFDs are English-centric; simple translation may mis-specify moral categories in other languages (Matsuo et al., 2018, Zangari et al., 20 Sep 2024).
• Normalization ambiguity: Variation in denominator choice (tweet length, word count, or number of moral-tweets) can complicate cross-paper comparison; explicit normalization reporting is recommended (Mutlu et al., 2020).
• Virtue/vice differentiation: Some lexicons (notably LibertyMFD) lack explicit polarity separation, while others (eMFD, MoralStrength) provide explicit vice/virtue values (Araque et al., 2022).
• Validation and calibration: Local validation against in-domain annotated ground-truths is critical before generalization across genres or languages (Duan et al., 2023).

6. Integration with Pre-Trained LLMs and Future Extensions

The MFD is central to modern efforts at “moralizing” pre-trained LLMs (PLMs):

• Feature augmentation: Lexicon scores are concatenated to PLM encodings for downstream classification (Zangari et al., 20 Sep 2024).
• Pseudo-labeling and weak supervision: MFD-based labeling of large unlabeled corpora enables efficient PLM fine-tuning on moral dimensions.
• Prompting: MFQ or key MFD items are used as prompts to elicit model-based moral judgments.
• Dynamic, continual expansion: Active research seeks to use PLMs to propose novel moral terms via masked language modeling, with human-in-the-loop validation (Zangari et al., 20 Sep 2024).
• Rich diagnostics: Embedding-based pipelines (e.g., vec-tionary) enable finer measurement—strength, valence, ambivalence—beyond mere foundation presence (Duan et al., 2023).

Prospective developments target robust explanation and chain-of-thought rationales, improved cross-lingual lexicons (with back-translation and multilingual embedding alignment), expanded virtue/vice calibration for new dimensions (e.g., Liberty), and hybrid methods integrating rule-based, lexicon-based, and PLM-based modeling (Araque et al., 2022, Duan et al., 2023).

7. Application Domains and Impact

The MFD and its expanded forms are widely deployed for:

• Social media discourse analysis: Quantifying moral rhetoric and polarization in Reddit, Twitter, and vaccine debates (Stanier et al., 29 Jun 2024, Mutlu et al., 2020).
• Topic modeling and narrative tracking: LDA and n-gram collocation analysis to reveal characteristic patterns of moral framing (Stanier et al., 29 Jun 2024).
• AI-generated content moderation: Detection of latent moral intuitions and identification of taboo, harmful, or questionable content (e.g., deepfake discussions) (Gamage et al., 2023).
• Comparative cross-cultural moral psychology: J-MFD enables side-by-side analysis of Japanese and English texts (Matsuo et al., 2018).
• Predictive analytics: Foundation-aligned metrics improve retweet prediction, persuasion modeling, and rumor/stance detection (Duan et al., 2023).
• PLM alignment and explainability: Acting as both supervision and rationalization tools in large-scale LLMs (Zangari et al., 20 Sep 2024).

Taken together, the MFD and its offshoots constitute a foundational resource and proof-of-concept for computationally tractable moral-psychology analysis, bridging traditional lexicography, linguistics, and contemporary NLP.