Historical reconstruction of human moralization with word association and text corpora

Domain: neuroscience
Published: 2026-01-07
Editorial outcome: major_revision

Abstract
Moralization, the process by which concepts and practices gain moral attributes, plays a pivotal role in shaping individual and social behaviour. However, research on how moralization unfolds over time remains limited. We present HistMoral, an open-access computational framework based on human word association, historical text corpora, and graph neural networks that enables scalable, retrospective analysis of moral trajectories of many different concepts. We apply our framework to analyze the moral time courses of over 20,000 concepts within the Corpus of Historical American English over the past 150 years, as well as within the New York Times annotated corpus from 1987 to 2007. Our findings provide robust evidence of moralization across diverse categories, from diseases to world leaders, and identify moralization around economic-political shifts of recent decades.

Introduction
Moralization is the process by which concepts, practices, and entities gain moral attributes over time1,2. How this process unfolds is one of the oldest mysteries of human nature3,4. Many studies have examined moralization within controlled lab settings at a single time point or over short time periods, typically focusing on individual topics such as vegetarianism, abortion, and genetic engineering1,5–9. These empirical investigations have shown that moralization arises from psychologically grounded mechanisms, such as moral emotions10,11, and reasoning about harms and benefits1,6,12. While these studies offer valuable insights into the psychological mechanisms driving moral change, they are inherently constrained by limited sample sizes, short timeframes, and a narrow range of moralized concepts. For instance, moral perceptions of cigarette smoking shifted gradually over decades from morally acceptable to morally inappropriate in many contexts10. Additionally, many issues were moralized decades or even centuries ago, such as opposition to slavery or dueling13, meaning that studying them is beyond the scope of current methods. These limiting factors raise natural questions regarding the generalizability of findings, leaving open important issues about how moralization unfolds over longer timescales and across a broader set of subjects. To address this important question, we introduce an open-access framework that automatically reconstructs the historical moral trajectories of lexicalized concepts (see Fig. 1a for illustrations), enabling efficient and systematic discoveries about when and why moralization occurs.

To study human morality at a broader scale, recent research in artificial intelligence (AI), particularly natural language processing, has utilized big data analysis and language models to automatically infer human moral values from textual data. This emerging field of automated moral inference in computer science employs computational tools for the automated analysis of morality. It includes investigating the rise and fall of moral concepts over time through large-scale word frequency analysis14 or word co-occurrence statistics15, using word-embedding and neural networks to predict moral sentiments from text16,17, and aligning AI systems with human values and moral decisions18.

Most relevant to our study, previous work has shown that language models pre-trained on large text corpora embed information that reflects people’s moral perception toward concepts such as “becoming a good parent” or “harming people”19. Other studies drawing on Moral Foundations Theory20 have used word co-occurrence statistics to study changes in moral relevance of words15, or used diachronic word embeddings derived from historical texts21. However, a main limitation of these computational approaches is that they are typically detached from psychological theories of moralization and lack the semantic richness necessary for robust, reliable predictions across long term horizons. These issues raise critical questions regarding how applicable these models are in service of exploring human moralization.

To overcome the challenges above, we introduce a computational framework that automatically reconstructs the moral trajectories of different concepts within the English lexicon over time. Our model builds on Moral Foundations Theory, which holds that moral judgments often arise immediately from intuitive, evolutionarily shaped foundations20,22, while also incorporating insights from psychological research on moralization, which examines how and why issues acquire or lose moral significance over time. Specifically, we draw on large-scale psychological datasets of word association—semantic networks that relate concepts with differing strengths based on their associations in people’s minds—which forms the basis of moral network graphs that indicate a concept’s moral properties23,24. Importantly, recent work has shown this graph-based model of moral association based on psychological accounts of moralization can predict human moral perception with precision25. For instance, the term smoking is strongly associated with words like unhealthy, addiction, and bad in English word association data26, indicating that when people see the word smoking, they tend to immediately think about its harmful health effects (see Fig. 1b for an illustration).

Although this moral word association approach provides an innovative way of uncovering the moral properties of a concept, one main challenge is how to extend this approach in a historical setting where word association data is unavailable. This limits our ability to understand how concepts may have shifted in their moral relevance over time, to identify what psychological and historical factors contribute to the moralizaton (or unmoralization) of a concept, or to build models that can predict future moralization of concepts. Addressing these limitations requires moving beyond models that capture only a single point in time, toward an approach that characterizes moralization across historical periods.

Here, we present HistMoral: a computational framework that addresses these challenges and reconstructs historical moral trajectories across time. Our framework extends the word association approach to historical moral inference. We do so by “approximating” word association networks using available large-scale linguistic data across historical periods. This approximation process works by assigning new associations to words based on how often words co-occur in a historical corpus. However, since co-occurrence statistics are inherently noisy predictors of conceptual relations27, we enhance this approximation by extracting contextual semantic representations (i.e., embedding vectors) using contextual language models such as BERT28. These embedding vectors capture word meanings based on usage across textual data.

To derive morally relevant information from these contextual representations and co-occurrence patterns, we train a graph neural network (GNN)29 to assign moral association scores to words—scores that reflect human moral perception. Figure 1b illustrates HistMoral framework and its psychological grounding using a representative example. Historical textual corpora reveal how frequently words appear in proximity to one another, while word association networks, derived from large-scale human experiments, capture conceptual relations between concepts. Strong associations with moral terms (e.g., associating smoking with bad) capture perceived moral relevance. Our reconstruction algorithm leverages textual corpora to approximate word associations and infer moral association scores for a wider range of concepts. We describe the details of our methodology in “Methods” Section.

Our approach offers interpretable and historical estimates of moral association scores that are theoretically aligned with human moral cognition. Table 1 presents a comparison of our framework with alternative computational methods. While lexical approaches, such as word co-occurrence frequency statistics, can capture temporal dynamics in diachronic corpora, they lack the semantic richness required for reliable moral inference. AI-driven methods, including early word embedding models21 and more recent large language models19, encode such contextual semantic information but primarily rely on pattern recognition from large-scale data, without a theoretical grounding to justify and explain their moral estimates. In contrast, our framework, which is grounded in psychological moral theories and word association datasets, leverages contextual semantics from text corpora to enable diachronic inference. Our framework can also be applied across diverse diachronic datasets, which is infeasible for pre-trained language models. As shown in the Supplementary Table 1, our method also achieves more accurate moral reconstructions than alternative computational approaches.

The output of our framework is the reconstructed moral trajectories of different concepts. Specifically, at each time point, we estimate two types of moral association score defined according to existing work21,25 as the predicted outputs from the model: moral relevance and moral polarity. Moral relevance approximates the proportion of moral terms (e.g., bad, addiction) that human participants associate with a given query concept (e.g., smoking). This score reflects the relative frequency at which a query concept is associated with morality in people’s minds. A higher moral relevance score indicates a stronger tendency for people to associate the query concept with moral terms. For example, the concept killing has a high moral relevance score because most people associate it with moral terms like wrong, crime, and evil. Moral polarity is defined as the ratio (or relative frequency) of a query concept’s association with morally positive terms (e.g., caring) compared to negative terms (e.g., harming). A higher score indicates a stronger tendency for people to associate the concept with morally positive terms compared to negative ones. For example, the concept killing has a low moral polarity score, because most of its word associates are negative words (e.g., crime, evil, and wrong). The concept helping on the other hand, has a high moral polarity score since most of its word associates are positive words (e.g., good, kind, and nice). In “Methods,” we provide a detailed description of how both moral relevance and moral polarity are calculated and the set of moral terms used for our analysis. Figure 1a illustrates how our framework identifies dynamically changing moral relevance scores throughout the past 150 years for three different concepts.

In summary, this work presents a computational framework that reconstructs historical moral trajectories across time. By combining the word association approach with a machine learning framework trained on historical corpora, we generate moral time courses that capture how different concepts have gained or lost moral relevance throughout history, and whether that relevance reflected primarily positive regard or negative judgment toward the concept. We validate our framework by demonstrating its ability to accurately predict known cases of moralization, and we apply it to systematically analyze moral change across thousands of concepts over the past 150 years.

Results
We focus on two large-scale longitudinal text corpora: the Corpus of Historical American English (COHA), a historical corpus containing approximately 400 million words and covering from the 1850s to the 2000s split by decades30, and the New York Times Corpus (NYT), which includes 1.8 million news articles published between 1987 and 200731. We include the details of these corpora in “Methods”. We chose to analyze COHA and NYT for their comprehensive, large-scale diachronic textual data that span long periods of time and are available to researchers. In practice, however, our framework is highly flexible and can be applied to any large-scale longitudinal text corpora.

Below, we first evaluate our framework trained using COHA and NYT against empirical moral association scores from contemporary human data. We then demonstrate how our framework can be applied to the analysis of moral time courses of different concepts and conceptual categories. Furthermore, we show that our framework discovers systematic patterns in historical moralization and identifies moral change associated with economic and political shifts.

Framework validation across empirical and historical trends
To validate our framework, we compare our model’s results with empirical human word association data. Then, we move onto examining whether our framework can effectively predict high moral relevance and polarity scores for concepts well-known to have high levels of each in historical contexts.

We first evaluate the model-predicted scores against the empirical scores computed from human word association data collected between 2011 and 201826 based on procedures in prior work25. Figure 2a,b show that, for both moral relevance and polarity, the model prediction based on reconstruction from the COHA corpus correlates strongly with the empirical measurements of moral relevance (r2=0.445, Pearson’s r=0.669, P < 0.0001, 95% CI = [0.632, 0.703], n=937) and moral polarity (r2=0.447, Pearson’s r=0.675, P < 0.0001, 95% CI = [0.637, 0.710], n=843). Similar results observed with reconstruction from the NYT corpus (moral relevance: r2=0.414, Pearson’s r=0.645, P < 0.0001, 95% CI = [0.604, 0.682], n=883; moral polarity: r2=0.439, Pearson’s r=0.665, P < 0.0001, 95% CI = [0.624, 0.702], n=789). Of note, our model predicted concepts including sinner, unfair, and divorce—which are strongly associated with morality—as more morally relevant than morally neutral concepts such as exist. Our model also predicted concepts like embrace, candy, and violence to fall within a gradient of moral polarity, ranging from positive to negative. In Supplementary Table 1, we provide additional evidence for the effectiveness of our model reconstruction in comparison to alternative model architectures and baseline models.

Along with reconstructing moral scores, we used sentiment analysis to extract word valence (positiveness and negativeness) at each historical time point to account for the potential confound of valence in our experiments. When examining all concepts with scores averaged over time, we found that valence was weakly negatively related to moral relevance (Pearson’s r=−0.140, P < 0.0001, 95% CI = [−0.153, −0.126], n=20,788). Valence was moderately positively related to moral polarity (Pearson’s r=0.445, P < 0.0001, 95% CI = [0.434, 0.456], n=20,788). However, the association between valence and moral polarity is not so high as to consider the two constructs redundant.

Next, to further test the effectiveness of our model, we assess whether our model reconstructs historical moral association scores by identifying certain concepts to be more likely to be moralized than others over the course of history. In particular, we focus on analyzing the concepts pertaining to “disease (health)” and “political figure (politics),” which have been consistently reported in the literature as being of high moral relevance8,32–40. The purpose of focusing on these topics is to offer confirmatory and qualitative validation of our computational framework. Specifically, we would expect our model to give such well-established moralized terms a very high moral relevance score. To this end, we identify words from the conceptual categories “disease” and “political figure,” and compare their degree of moral relevance to randomly selected word sets of equivalent size from the COHA corpus. We use the normalized moral scores with z-score standardization for our analyses (see Methods for details).

Figure 2c compares the model-estimated moral relevance of disease-related concepts (e.g., cholera in the 1990s) with a random set of concepts matched in set size. The results indicate that disease terms exhibit higher moral relevance than the control set of random concepts (Wilcoxon signed-rank test: W=8927.0, P < 0.0001, one-tailed; Cohen’s d=2.24, sample size=135). A non-parametric test was used as the paired differences violated the normality assumption. To validate these findings, we repeated the analysis across 1000 randomly constructed control sets. The distribution of mean moral relevance scores from these random samples was approximately normal (Shapiro-Wilk: W=0.998, P=0.316, Mean = −0.020, SD=0.081, n=1000). A one-tailed one-sample t-test confirmed that the disease terms showed significantly higher moral relevance than the average of random control sets (t(999)=−684.400, P < 0.0001).

These results help validate our model by showing that it provides high moral relevance scores to concepts known to be highly moralized based on previous research on the moralization of diseases32. Additionally, for moral polarity, disease-related concepts were perceived to be more morally negative compared to the randomly constructed sets of concepts (one-sample t-test: t(999)=929.996, P < 0.0001, one-tailed). Although the Shapiro-Wilk test indicated deviation from normality for these random sets (W=0.995, P=0.003), the effect size and sample sizes are both large (Cohen’d=29.41, sample size=1000). This finding shows that the model-reconstructed moral polarity reflects a negative moral perception toward diseases. We repeated this analysis by mapping each disease term to one of the top 5 (non-disease) terms with the most similar valence scores. Our results remained robust for both moral relevance (one-sample t-test: t(999)=−711.96, P < 0.0001, one-tailed) and moral polarity (one-sample t-test: t(999)=428.60, P < 0.0001, one-tailed), indicating that our findings cannot be explained by valence alone.

We observe similar trends for political figures (i.e., world leaders such as Winston Churchill). Figure 2d compares the moral relevance estimates for various political figures with a random sample of first names of the same size, showing that political figures generally have higher moral relevance (Wilcoxon signed-rank test: W=1681.0, P < 0.0001, one-tailed; Cohen’s d=0.75, sample size=65). Repeating this analysis across 1000 randomly constructed sets of names further confirmed that political figures have stronger moral relevance (one-sample t-test: t(999)=−223.940, P < 0.0001, one-tailed).

To further evaluate the effectiveness of our framework in capturing historical shifts in moral relevance, we analyzed how major world events influence the moralization of political entities. Using the Historical Conflict Event Dataset41, we find that entities involved in conflicts exhibit higher moral relevance and more negative moral polarity during wartime compared to peacetime (moral relevance: Wilcoxon signed-rank test W=13876.0, P < 0.0001, one-tailed; Cohen’s d=0.15; moral polarity: Wilcoxon signed-rank test W=2712.0, P < 0.0001, one-tailed; Cohen’s d=0.17, sample size=183). Figure 2e illustrates these changes, with the y-axis indicating the difference in moral relevance between wartime and peacetime. For instance, the term Axis—referring to the Axis Powers in World War II—shows a substantial spike in moral relevance during the 1940s, as shown in Figure 2f. A second peak appears in the 2000s, possibly due to the usage of the phrase “Axis of evil” by then-president George W. Bush.

We also observe that the losing side in international conflicts is associated with both higher moral relevance and more negative moral polarity than the winning side (moral relevance: Wilcoxon signed-rank test W=9508.5, P=0.038, one-tailed; Cohen’s d=0.20; Wilcoxon signed-rank test W=13790.5, P=0.001, one-tailed; Cohen’s d=0.27, sample size=210). These results indicate that our model assessed the moral relevance and polarity of these concepts in line with what would be expected given our understanding of the moralization of political entities during wartime. To account for the potential confounding effect of valence on moralization during conflicts, we trained a binary logistic regression model to predict conflict outcomes (winner vs. loser) using differences in sentiment, moral polarity, and moral relevance scores between the two sides as predictors. The model achieved an accuracy of 0.59 (random baseline=0.5, sample size=207). Notably, only moral polarity emerged as a statistically significant predictor (β=0.34, P=0.036, 95% CI=[0.022, 0.653]), while sentiment and moral relevance were not statistically significant predictors (P=0.41 and P=0.82, respectively). These results suggest that conflict outcomes are more likely to be framed in terms of moral righteousness, even when controlling for sentiment. Further details of these analyses are provided in the “Methods” Section.

Since our framework draws on word co-occurrence patterns to reconstruct moral association, we further investigate whether our reconstructed moral estimates are artifacts of contextual co-occurrence with moral words (indirect or surface-level association) or directly capture semantic-level moral association. In other words, we examine whether our model is simply capturing that certain terms often appear near moral words, or if our model is actually capturing conceptual relationships with moral values and concepts. To address this, we performed an analysis that distinguishes between direct and indirect types of moralization. Specifically, we consider direct moralization when a term becomes moralized conceptually as a result of conceptual association with moralized ideas. We consider indirect moralization when a term’s moral relevance increases due to frequent co-occurrence with moral terms in our textual data, but does not demonstrate a deeper association with moralized ideas. For example, the word cigarettes may co-occur with words like law, and regulation, but not yet be conceptually moralized in society. We find that our moral relevance scores (proxy of direct moralization) and indirect moralization scores are moderately positively correlated across time (Pearson’s r=0.53, P < 0.0001, 95% CI=[0.526, 0.532], n=168,767). This outcome aligns with the intuition that moralized terms tend to appear in moral contexts. However, focusing specifically on diseases and named political figures, we find no statistically significant correlation between the two (diseases: Pearson’s r=0.083, P=0.337, 95% CI=[−0.087, 0.249], n=135, political figures: Pearson’s r=0.036, P=0.794, 95% CI=[−0.234, 0.301], n=54). This result suggests that our moral relevance estimates and indirect moralization scores do not always move in the same direction, and that our model is capturing direct moralization of the target concept.

This set of results shows that our model is a valid predictor of moral associations. As we showed in various ways in these experiments, our model-reconstructed moral relevance and moral polarity not only predict empirical moral association scores, but are also in line with what is expected to be a subject of moral judgment across time.

Systematic moralization of conceptual categories
Our findings thus far show high moral association scores for categories such as “disease” terms. This suggests there may be shared trends in moral trajectories of concepts within the same category. To explore this pattern more thoroughly in the lexicon, we examine the moral relevance scores of a comprehensive set of related concepts or conceptual categories. This analysis is aimed at characterizing the similarities and variations across a diverse range of concepts and evaluating the utility of our framework for making discoveries about systematic patterns of moralization.

To define conceptual categories, we used an existing comprehensive database that contains 117 distinct groups of concepts, where groups/categories are selected from previous categorization literature42–48, and normed concepts within each category are empirically collected from human participants49. We next demonstrate the effectiveness of the model in reconstructing human moral perception at the category level. We did so by using our model to compute the averaged category-wise moral relevance scores and validated them against human data26. A Spearman’s correlation test indicates that our model reconstruction correlates strongly with the moral relevance scores obtained from procedures used in existing work25,26 with ρ=0.744 (P < 0.0001, 95% CI=[0.65, 0.82]). For comparison, we also used a state-of-the-art large language model, GPT-4O, to reconstruct the same empirical data and found a substantially weaker, but statistically significant correlation with ρ=0.565 (P < 0.0001, 95% CI=[0.43, 0.68]). See Fig. 3a for a summary of these results and Supplementary Notes Section 1.2 for details of this analysis.

Figure 3a visualizes the moral relevance scores estimated by the model for all of these categories with respect to their rates of change in moral relevance between the 1850s and 2000s. The rates are calculated from regressing the moral relevance scores against time, and therefore show the average growth in moral relevance in one unit of time (i.e., a decade). As shown, the overall moralization patterns vary across conceptual categories. For example, the conceptual category “disease” exhibits both high moral relevance and a high rate of change, suggesting it has become increasingly morally relevant over time. In contrast, the category “supernatural being,” which includes concepts like god, devil, ghost and alien, is morally relevant but shows a decline in moral relevance over time. Similar to our analysis, previous work has shown that the frequency of English moral terms tend to fluctuate with respect to major world events in the 20th century14. Therefore, we further investigate whether our estimated moralization rates might simply reflect increases/decreases in frequency of certain moral terms associated with these categories. Using the frequencies (drawn from COHA) of concepts in the conceptual category database across time, we find a weak positive correlation between word log frequency and moral relevance score (Pearson’s r=0.084, P < 0.0001, 95% CI=[0.068, 0.100], n=14,659). Our examples in Supplementary Fig. 1 further show that frequency levels do not reliably predict moral association scores of conceptual categories such as “disease” and “supernatural being.” These results support the interpretation that observed declines (or rises) in moral relevance scores reflect changes in the moral framing and perception of concepts over time, rather than just shifts in word usage frequency.

To better understand whether there is systematic moralization in the conceptual categories, we performed a predictive analysis using moral relevance and moral polarity scores of a concept to predict category membership based on its proximity to other concepts in the moral space. Our results show that category membership can be predicted with an accuracy of 46.7% based solely on moral association profiles, outperforming the majority vote baseline, which achieves 9.1% accuracy by always predicting the most common category in the data (see Supplementary Fig. 2). Figure 3b further shows that similar concepts exhibit synergistic movement in moral trajectories. The heatmap, derived from pairwise correlations in the moral trajectories of concepts across different conceptual categories, suggests that certain conceptual categories share correlated moral trajectories over time. For example, concepts in the “family relationship” category have similar moralization trajectories to each other, but are also similar to those of concepts in the “social relationship” category. A comparison of average pairwise correlations within these conceptual categories versus randomly generated categories of equivalent sample size reveal that concepts within the same category exhibit more similar moral trajectories than concepts across different categories (one-sample t-test: t(999)=−203.94, P < 0.0001, Cohen’s d=4.45, two-tailed; see “Methods” for additional details).

Moral change under economic and political shifts
We next demonstrate the utility of our framework in identifying moral changes over recent decades, focusing on the NYT corpus from 1987 to 2007. Specifically, we study the relationships between economic and political shifts with moral trajectories, since both factors are known to correlate with people’s moral views50–55.

To explore the relationship between moral change and the economy, we focus on consumer price fluctuations of retail products in the United States. Our experiment is motivated by past research findings showing that as products become morally negative, the amount people are willing to pay for them decreases56, meanwhile, as products become more morally positive, people are willing to pay more for them57,58.

Using our framework to study such effects, Fig. 4a shows that annual changes in retail prices have a clear negative relationship with changes in moral polarity (Pearson’s r=−0.144, P=0.0001, 95% CI=[−0.216, −0.071], n=704). Figure 4b further illustrates this relationship for common product categories including “Gasoline,” “Bread,” and “Beef.”

Training an ordinary least squares (OLS) model with product ID, current year, price of the product in the previous year, and sentiment valence scores as control variables shows that year-by-year changes in moral polarity predict changes in product pricing (β=−0.147, P=0.016, 95% CI=[−0.266, −0.027], t(335)=−2.419). The negative coefficient indicates that changes in moral polarity of a product are inversely related to changes in its retail price. Specifically, when a product becomes associated with more morally positive concepts relative to the previous year (i.e., positive change in moral polarity), its price tends to decrease. Conversely, when a product becomes associated with more morally negative concepts relative to the previous year (i.e., negative change in moral polarity), its price tends to increase. See “Methods” and Supplementary Eq. 1 for more details on the dataset and analysis.

Although these findings offer insights into moral-economic dynamics, they should be interpreted with caution. While prior work has shown that people are less willing to pay more for morally negative items56–58, we find the opposite pattern, that moral negativity predicts higher retail prices. However, our analysis does not establish causal directionality, in part due to the limited long-term price data across the products. The observed relationship could reflect different possible mechanisms, such as price increase leading people to view products more negatively, vice taxes inflating the price of morally negative products, or common external factors affecting both morality and retail prices. Moreover, our analysis focuses on actual retail prices, which may not fully capture consumers’ underlying willingness to pay. To probe this finding further, we conducted a qualitative investigation of the NYT corpus and found instances where price fluctuations were explicitly discussed in moral terms. For instance, price increases were linked to environmental concerns and political rivalry (e.g., energy prices in the 2000s) and public health issues (e.g., red meat contamination in 2005), reflecting the potential influence of common external factors on both price and moral perception.

Next, we explore the relationship between moral change and political discourse in the United States. Building on our analysis of historical world events, we hypothesize that impactful modern political events—such as Congressional debates and presidential elections—should influence changes in public moral perception (and vice versa). To test this hypothesis, we analyzed the changes in moral association over time in two key political contexts: (1) Congressional speeches and (2) presidential election cycles in the United States.

We draw on the United States Congressional speech data to identify common political topics and phrases59. Figure 4c illustrates 22 politically relevant topics recorded in this dataset. For example, economical challenge is a phrase from the topic “economy.” First, we hypothesize that concepts gaining moral relevance would also gain prominence in political discourse. To test this hypothesis, we performed an OLS analysis predicting annual changes in the frequency of words in Congressional speeches based on shifts in their moral association scores, while controlling for sentiment scores. Our findings reveal a statistically significant positive relationship between annual changes in words’ moral relevance scores and their frequency in Congressional speeches (β=0.499, P < 0.0001, 95% CI=[0.378, 0.621], t(3,257)=8.079). We also find a statistically significant negative relationship between annual changes in moral polarity scores and word frequencies based on Congressional speeches (β=−0.141, P=0.022, 95% CI=[−0.261, −0.021], t(3,257)=−2.300). These results show that words experiencing increases in moral relevance (i.e., becoming more moralized) and declines in positive moral sentiments are also more frequently mentioned in Congressional speeches and debates, which suggests an interplay between moralization and political discourse.

We also examine patterns of moralization in relation to U.S. presidential election cycles. Figure 4c shows changes in moral relevance across 22 politically salient topics, comparing years when Democratic candidates won the presidency (1992, 1996) to those when Republican candidates prevailed (1988, 2000, 2004). We observe that topics such as the “economy,” “federalism,” “environment,” and “education” exhibit higher moral relevance in the NYT in years corresponding to Democratic victories. Since the presidential elections are held in November, the estimated moral relevance scores mostly measure moral association in months leading to the election, and therefore likely predict the upcoming election results. To disentangle these effects from broader temporal trends, we conducted a controlled mixed-effects OLS analysis to estimate the impact of election outcomes on moral relevance scores in the upcoming year (see Supplementary Eq. 3 and Supplementary Table 11 for details). This analysis reveals two key findings: (1) moral relevance scores are higher in the year of the election and become lower after the election, and (2) election outcomes have differential effects across political topics. For instance, environmental issues have lower moral relevance scores in months leading to Republican victories, but their moral relevance scores increase after the election, potentially reflecting intensified moral framing by the losing Democratic party.

Figure 4d illustrates phrases with the largest differences in moral relevance under Democratic wins (top) versus Republican wins (bottom) in the year of the elections. In Democratic victory years, concepts like drug show an increase in moral relevance compared to Republican victory years, whereas concepts like HIV are more morally relevant in Republican victory years (see “Methods” for details). We believe these results may reflect broader political dynamics and unobserved temporal effects. For instance, past work finds that among the congressional representatives in the United States, the use of moral language increased after one’s political party lost an election60. Therefore, it is likely that the opposition party that most fears it will lose the upcoming election (and does so) is more likely to push a moralized agenda. Note that these patterns reflect the moral framing of these concepts as represented in The New York Times—a newspaper generally associated liberal-leaning stances—which might be different from the party leaders themselves.

In additional analyses described in the Supplementary Notes Section 1.6, we examined the temporal relationship between moral change and social concerns about relevant moral issues. Our findings show a correspondence between increases in moral association and growing social concerns as recorded in empirical surveys. Furthermore, a bottom-up analysis described in Supplementary Notes Section 1.7 based on both NYT and COHA corpora shows that concepts with the sharpest increases in moral relevance are mostly relevant to major socio-political events and movements (see Supplementary Tables 7 and 8).

Methods
Longitudinal text corpora
We used two longitudinal corpora—the Corpus of Historical American English (COHA)30 and the New York Times Corpus (NYT)31—to reconstruct historical moral time courses. COHA, one of the largest structured corpora of historical English, contains over 475 million words across 100,000 documents, and it is organized by decade from the 1810s to the 2000s. Due to document sparsity in the earliest decades, our analysis starts in the 1850s. An alternative to COHA is the Google Books Corpus, which spans a broader period (1500s to 2000s). However, it is limited in that it provides sentence fragments of up to five words (5-grams) as opposed to complete sentences. The NYT corpus serves as a modern longitudinal dataset, spanning from 1987 to June 2007, with over 1.8 million articles. For consistency, we balanced the NYT documents by year. Both of these corpora are tokenized by the publishers. We further extracted the sentences, removed all non-alphanumeric characters, and lemmatized all the tokens using NLTK toolkit67.

Computation of moral scores based on word association data
Our framework builds on previous research in moral association graphs, which is limited to estimating moral scores based on word association at a single point in time25. Here, we describe how the moral scores are computed. For a given query concept, the moral relevance score represents the proportion of morally significant response words (e.g., bad) associated with the query word (e.g., smoking). The moral polarity score reflects the balance of morally positive and negative words linked to the query word. For instance, for a query c associated with T response terms in a word association network, if M of those responses are moral concepts, the moral relevance score is then M/T. From these M responses, if P are morally positive and N are morally negative, the moral polarity score for c is then defined as P−N + 1 / 2T + 2. Under this formulation, words associated with an equal number of positive and negative moral words receive a moral polarity score of 0.5, while words associated solely with positive or negative moral responses have scores of 1 or 0, respectively.

The set of moral terms used in our analysis was compiled based on computational research on morality16,65,68–70 and was developed for moral analysis based on Moral Foundations Theory20. This theory proposes five core foundations to explain universal and culturally specific moral views. These foundations, each with virtue and vice polarities, are Care/Harm, Fairness/Cheating, Authority/Subversion, Loyalty/Betrayal, and Sanctity/Degradation. We used these moral categories to detect morally positive and negative terms within our dataset. However, since words in the MFD can be classified under moral foundations that may not align with their overall sentiment (e.g., war is classified under the Loyalty foundation, a virtue), we used an external dataset of valence ratings71 for refinement. This dataset provides empirical valence scores for over 13,000 English words, ranging from 1 (most unpleasant) to 9 (most pleasant), with a mean of 5.06 and a median of 5.2. For our analysis, MFD virtue words with valence ratings below 4.5 (e.g., war) were reclassified as negative, and vice words with valence ratings above 6 were reclassified as positive. Additionally, we excluded specific MFD terms (family, mother, father, child, character, protection, cry, clean, cleaning, cleaner, order, dirt, and community) due to their frequent use in non-moral contexts, which could potentially distort the reliability of our moral association metrics. By incorporating both MFD version 1 (which includes a category for general moral terms such as morality) and version 2, which contains an expanded lexicon, we compiled a total of 1718 moral terms, comprising 703 positive and 994 negative terms (general moral terms are not polarity annotated).

For estimating moral scores, we used the Small World of Words (SWOW) dataset26, which contains association data for over 12,000 English words, with each word linked to 100 associative responses. To ensure alignment between our textual corpora and the word association dataset, we lemmatized words in SWOW using the NLTK package.

Reconstruction model with graph neural networks
To extend the moral association graphs for reconstructing moral scores through historical times, we developed a new graph-based model using graph convolutional network (GCN)29. The GCN takes textual representations and word co-occurrence data as input, with target values set to empirical moral scores (moral relevance and moral polarity) from human word association data. After parameter training, this model learns a mapping between moral association and corpus-based co-occurrence data, and it then generalizes this learned mapping to a historical context and therefore estimates moral scores across different time points in a longitudinal corpus (beyond the period in which it was trained on where word association data is available).

During training, the GCN model takes an adjacency matrix A of a weighted input graph and a feature vector X. The model then generates new representations or target values for each node through a layer-wise propagation rule, expressed as: H(l+1)=σ(D~−1/2 A~ D~−1/2 H(l)W(l)).

Here, A~ represents the normalized adjacency matrix A with added self-connections, and D~ is a diagonal matrix where D~ i,i = Σj A~ i,j, and σ stands for a non-linear activation function. The hidden representations of nodes at each layer (l) are represented by H(l), with the H(0) being the input feature matrix X. The GCN’s parameters are the layer-wise weight matrices W(l), and these are optimized during training. We use a GCN with two layers and a hidden representation dimension of 128.

In our framework, the adjacency matrix A is constructed based on word co-occurrences (excluding stopwords) within a given snapshot of time in a text corpus. For each word, we identify its top 100 neighbors with the highest degrees of co-occurrence, using a context window of size 1 (bigram co-occurrence). The edges are weighted according to the positive point-wise mutual information (PPMI) metric. Specifically, for words wi and wj, the adjacency weight is defined as A i,j =max(0,log p(wi,wj)/p(wi)p(wj)) if wi and wj are within each other’s top 100 terms, and A i,j = 0 otherwise. The adjacency matrix is then constructed as a matrix with self-connection loops, by normalizing the matrix A~+D~ so that each row sums to 1.

The input feature matrix X is constructed based on the contextual representations of words within each time point in the text corpus. These representations are distributed semantic vectors generated by pre-trained large language models, which capture the meanings of words based on their surrounding context. In particular, we use masked language models like BERT28, which take sentences as input and generate contextualized representations for each token. In our framework, we feed sentences from the text corpus into a BERT model and retrieve word representations from the model’s final layer. Here, we report results using the bert-base-uncased model (accessed through HuggingFace’s transformers), but we find our results are robust across alternative models, as shown in Supplementary Table 1. We then take an average of each word’s contextual representations from all the sentences it appears in, and use these average vectors as rows of the matrix X.

The final representations of words are mapped to their respective moral scores through a linear transformation. We explore three alternative architectures for this mapping. The first architecture, denoted as the “baseline” model, maps the input feature matrix X directly to word association scores without employing the GCN. In the second architecture, referred to as the “GCN” model, the representations from the GCN’s final layer (H(2)) are mapped to moral scores. The third approach, the ‘Residual GCN’ model, combines the outputs from both the GCN and the input features by mapping H(2) + ReLU(XW), where W denotes a linear transformation that aligns the input feature space with the GCN’s output space. Evaluation results provided in the Supplementary Table 1 show that the “Residual GCN” model outperforms the ‘GCN’ and the baseline models for both moral relevance and moral polarity estimations. Therefore, we adopt “Residual GCN” model in our experiments.

For training, we use the most contemporary time point in each text corpus (the 2000s in COHA and 2007 in NYT), where word association data are available and can be aligned with textual data. Training targets are derived from empirical moral relevance and polarity scores based on human word association networks in SWOW. We identified 5864 cue words in SWOW with a moral relevance score above zero, and supplemented this set with 200 additional words with zero moral relevance scores for negative sampling. From this combined set, 4681 words were present in COHA, and 4821 were present in NYT. Similarly, for moral polarity, we identified 5099 cue words in SWOW with scores greater or less than 0.5, indicating varying degrees of moral positivity and negativity. This set was also supplemented with 200 additional words with moral polarity scores of 0.5, resulting in 4213 words available in COHA and 4821 in NYT.

For each cross-section of data (COHA/NYT × moral relevance/moral polarity), we reserved 20% of examples for testing, with results reported in the main text. The remaining 80% was used to train five models, each trained on a different 75/25 split of the data for training and validation, sampled randomly. To improve convergence, we log-transformed the moral relevance and polarity scores using the formula log(100p+1), where p is the score for a given word. We trained using a Gaussian negative log-likelihood loss function, treating the moral scores as samples from Gaussian distributions with predicted expectations and fixed variances of 1. We used a base learning rate of 0.01 with a decaying factor of 0.1 at steps 100 and 500. We trained the models for 10,000 steps and selected the checkpoints with the best performance on the validation set.

After training, we applied these models to historical corpora at different time points, with each time point comprising over 10,000 lexical items, including unigrams and a curated selection of bigrams. Specifically, at each time point in both the COHA and NYT corpora, we extracted the 10,000 most frequent unigrams. Additionally, we supplemented these items with specific concepts drawn from the Small World of Words dataset and a curated set of bigrams representing major global events, technologies, diseases, and notable figures (e.g., genetic engineering, civil war, yellow fever, President Obama, Joseph Stalin). These bigrams were identified using relevant Wikipedia entries, and we have included the full list in our repository to facilitate reproducibility. The inclusion of bigrams alongside unigrams was intended to capture a broader range of concepts that single-word terms alone could not adequately express. In the Supplementary Table 9, we provide the total number of extracted concepts (unigrams and bigrams) from COHA and NYT at each time point.

For each cross-section of data, we standardized the outputs from all five models using z-score standardization, reporting the final moral scores as the mean of these standardized predictions. Under this scheme, words with negative moral relevance scores are more morally irrelevant or neutral than average, while those with positive moral relevance scores are more morally relevant. Similarly, words with negative moral polarity scores are linked to more negative moral concepts, while positive moral polarity scores indicate words associated with more positive moral concepts relative to the global average.

Indirect moralization
We compare our reconstruction performance with a baseline that only uses contextual co-occurrence information. Specifically, we define a new metric called indirect moralization (denoted as Ind). To estimate indirect moralization at each time point, we use the normalized adjacency matrix A~, and gather the degree of co-occurrence with moral terms P (drawn from MFD) for each concept c: Ind(c)= A~ c,t 1(t∈MFD). By definition, indirect moralization lies in the range of 0 to 1, where 0 means none of the word’s nearest neighbors are moral terms, and 1 means all the word’s nearest neighbors are moral terms.

Sentiment scores
We use the SentimentIntensityAnalyzer module from NLTK library as a standard sentiment analysis toolkit. We assign the compound score from this module to the sentences in our textual corpora. The compound score is normalized within the range of −1 (most negative) to 1 (most positive). To estimate words’ sentiment scores at each time point, we take an average of the compound scores of all sentences the words appear.

Model evaluation
To evaluate the effectiveness of our framework across historical and empirical datasets, we excluded terms that occur fewer than 50 times at a given time point in COHA to ensure reliable model estimation. We identified disease terms using independent sources: (1) Wikipedia lists of diseases and epidemics, and (2) prototypical disease terms generated empirically by human participants49. The intersection of terms from these independent sources with COHA resulted in a set of 22 disease terms, which are anthrax, cancer, cholera, diabetes, diphtheria, flu, hepatitis, hiv, hivaids, influenza, leukemia, malaria, measles, plague, polio, salmonella, scarlet fever, smallpox, tuberculosis, typhoid, typhus, and yellow fever. In selecting the randomized control sets, each (disease, time point) pair was matched with a randomly selected non-disease term from the same time point, and this sampling procedure was repeated 1000 times.

Using Wikipedia, we identified a list of notable political leaders from the past century. Additionally, we included the presidents of the United States in this list. In the Supplementary Table 10 we present these leaders along with the query terms used to locate them in COHA. If a leader could be identified using multiple query terms (e.g., lenin and vladimir lenin), we calculated the average moral scores for these terms. To compare political leaders with other names, we use the SSA dataset of popular baby names in the United States. Similar to the disease analysis, each (political leader, time point) pair was matched to a randomly selected name from the same time point, where the time point was selected such that it corresponded to the duration of the political leader’s term in office. This sampling procedure was repeated for 1000 times.

To analyze the effect of political conflicts on moralization, we used the Historical Conflict Event Dataset41. This dataset offers a comprehensive description of conflicts over the world, in a timescale from 1468 BC to the invasion of Iraq in 2003. The dataset further provides geographic coordinates and the year(s) of the conflict, along with its participants and winning and losing sides (which are mostly country names). Conflicts are also annotated with the Lehmann Zhukov scale, which specifies the size of the battle72. Using this scale, we selected conflicts with Lehmann Zhukov scale of at least 3, which corresponds to battles with 20,000 to 100,000 men on either side to ensure sufficient representation of the battle in COHA. For example, the dataset identifies the term vietnamese as a participant in the Vietnam War during the decade of 1970s. In our experiment, for each participant term, we identified all the decades where the participant was involved in a conflict (wartime) and was not involved in any conflicts (peacetime). We then compared the average moral scores between wartime and peacetime using paired t-tests.

Analysis of conceptual categories
We used a category norm production database to examine how moralization occurs across related concepts. This database includes 117 conceptual categories (67 concrete and 50 abstract) populated with norms generated by 64 human participants, who were asked to recall as many members of each category as possible within 60 seconds49. The categories in this database were selected from previous categorization literature42–48, and spanned different taxonomic levels (e.g., “crime,” “non-violent crime,” and “violent crime”). This procedure resulted in a dataset of cognitively related concept clusters. We chose this database as opposed to the previous ones because it covered a larger set of categories balanced across concrete and abstract concepts, and was collected more recently, which aligns better with the period in which the word association data we used for training our models (SWOW26) was collected. To ensure the reliability of this crowdsourced database, we filtered out any concepts that were nominated by only a single participant within each category. To assess each category’s moral relevance, we averaged the moral relevance scores of all its member concepts over time points in the dataset. This approach provided an estimate of the overall moral relevance of each category and allowed us to compare categories in moral space. To evaluate the rate of moralization within each category, we regressed each category’s moral relevance scores against standardized historical time points from COHA, using the year coefficient as a measure of the category’s rate of moralization.

We concentrated our analysis on categories with an overall positive moral relevance score to examine whether concepts within these (morally relevant) categories exhibited similar moral trajectories. For this purpose, we calculated pairwise Pearson’s correlation coefficients for the moral relevance scores between concepts within the same category and across different categories. Correlations were restricted to concept pairs that overlapped in at least ten decades (equivalent to one century) within COHA. The results of these pairwise correlations are presented in Figure 3b, where intra-category (diagonal) and inter-category comparisons are both shown. Formally, the correlation values between categories of C1 and C2 (which can be the same) are calculated using the average correlation values between members w1 ∈ C1 and w2 ∈ C2: corr(C1, C2) = 1/N Σw1∈C1 Σw2∈C2 corr(w1, w2), where N is the number of pairs.

To assess whether concepts within the same category share more aligned moral relevance trajectories, we generated random categories with matched membership counts and compared the average intra-category correlation (i.e., weighted average of the diagonal entries in Fig. 3b). This permutation test was repeated 1000 times, with results reported in the main text.

Analysis of moral change
To investigate the relationship between moral change and fluctuations in consumer product price, we used average retail prices reported by the U.S. Bureau of Labor Statistics. This database offers monthly price statistics for various categories of food items, utility gas, fuel oil, electricity, and automotive fuels. To quantify the total annual change in product prices, we compared monthly prices to those from the same month in the previous year, calculating the average price change (relative change) for each product over the 12-month period, over the years overlapping with the NYT corpus. We matched each product item to a corresponding token that best represents it; for instance, the product “Apples, Red Delicious, per lb. (453.6 gm)” is mapped to the token apple. In the Supplementary Table 5, we show the list of products in this database along with their simplified names used in our analyses. Using these matched names, we estimated the annual changes in moral scores, which had been previously normalized through z-score standardization. This process yielded tuples structured as (product item, moral association change, relative price change, year of change). We only included examples where moral relevance of the product increased over the span of a year, in order to focus our investigation on items with sufficient moral relevance signals. This filtering resulted in a total of 704 examples.

To examine the effect of political shifts on moral change, we use the Congressional Record for the 43rd-114th Congresses database59. This database contains transcripts of speeches delivered on the floors of the United States House of Representatives and Senate from the 43rd to the 114th Congress. Additionally, it provides key unigrams and bigrams manually classified into 22 substantive political topics (topics shown on the y-axis of Fig. 4c). Since our framework primarily uses unigram concepts, with a small subset of manually selected bigrams, many bigram phrases in the Congressional Record database (e.g., veteran bill) are not represented in our lexicon (these concepts also tend to be sparsely represented in the corpora we analyzed). Furthermore, some bigrams are presented in wildcard formats (e.g., econom* challeng*), allowing for matches to multiple items (e.g., economical challenge and economy challenge). To estimate the moral scores for these bigrams, we calculated the average moral scores of their constituent words. For example, the moral relevance score of econom* challeng* was derived from the average of the moral relevance scores of economy, economical (first constituents), and the moral relevance score of challenge (second constituents).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Reviewer #1\n(Remarks to the Author)\n# Reviewer Report\n## Summary\nThe paper presents a computational framework designed to reconstruct the historical dynamics of moralization for a broad\nrange of lexical concepts. Utilizing word co-occurences and graph neural networks, the framework estimates moral\nrelevance and moral polarity from textual corpora, specifically the Corpus of Historical American English and the New York\nTimes corpus. The authors validate their approach against contemporary human moral association data and demonstrate its\napplication to over 20,000 individual concepts and 117 conceptual categories, covering a span of 150 years. The analysis\nreveals systematic patterns in moralization, highlighting links between historical events, economic factors, political contexts,\nand shifts in moral perceptions. The supplementary material provides additional methodological details, model evaluations,\nand extended analyses to substantiate the main findings.\n## Overall evaluation\nThis study makes an important contribution by addressing a gap in our understanding of moralization processes from a\nhistorical perspective. The computational framework developed by the authors is rigorous and convincing, and demonstrates\nclear utility for analyzing moral dynamics over time. The methodological choice to employ transformer-based embeddings is\nparticularly commendable and well justified, significantly strengthening the validity and interpretability of the results. The\nmanuscript is convincing and provides valuable insights into the dynamics of moral perceptions.\n## Results\nWhile the authors' methodological framework is solid and convincingly presented, the interpretation of their results\noccasionally lacks critical depth and contextual clarity. Below, we highlight three specific aspects where additional reflection\nand clarification could strengthen the overall contribution.\n### Neutrality and intuitiveness\nThe authors open their article by defining moralization as the process through which concepts \"that were previously morally\nneutral acquire a moral component,\" citing literature on vegetarianism to support this idea. Although this provocative\ndefinition immediately invites critical reflection—particularly questioning whether neutrality is genuinely achievable—such a\ndiscussion is notably absent from the article. Moreover, some of the examples chosen do not convincingly illustrate this\nprocess, as their historical neutrality remains questionable, creating a mismatch between the article's definition and its\nexamples.\nConsequently, several of the paper's highlighted results appear somewhat intuitive or even predictable. For instance, the\nauthors compare disease-related concepts with randomly chosen control terms, concluding that disease terms exhibit\nsignificantly higher moral relevance. They do not reflect on the fact whether terms like 'cholera' were every morally neutral. It\nis also hardly surprising that diseases have low moral polarity scores, given their inherently negative nature, unlike more\ncontested concepts such as 'nuclear weapons,' which invite moral debate. Similarly intuitive is the observation that losing\nsides in international conflicts are associated with more morally relevant and negative language compared to winners. While\nthese results are robust, the authors could strengthen their contribution by explicitly discussing the methodological or\ncomputational value of these predictable outcomes or by emphasizing more novel or counterintuitive findings from their\nanalysis.\n### Word co-occurrence limitations\nThe moral relevance and polarity scores calculated in this study rely heavily on word co-occurrence patterns within textual\ncorpora. However, this raises an important methodological concern regarding how moral relevance is attributed to specific\nterms. For instance, if political figures like 'Winston Churchill' exhibit high moral relevance scores, does this genuinely reflect\nChurchill himself being moralized, or is it merely an artifact of his frequent co-occurrence with inherently moralized\nconcepts? Consider the sentence, \"Winston Churchill condemned the spread of cholera as a tragic consequence of poor\nsanitation and neglect.\" Here, Churchill’s elevated moral relevance score would primarily derive from his association with\nthe negatively moralized concept 'cholera.' The authors could strengthen their analysis by explicitly addressing how their\nmethodology distinguishes between direct moralization of a term and indirect moralization resulting from contextual\nassociations. Clarifying this distinction would significantly enhance the interpretability and methodological rigor of their\nframework.\n### Interpreting declines in moral relevance\nThe authors note that the category \"supernatural being\" exhibits moral relevance but experiences a decline over the studied\nperiod. We suggest contextualizing this finding with existing literature, specifically the work by Wheeler et al. (2019), who\ninvestigate the historical rise and fall of moral concepts between 1900 and 2007. Based on the findings of Wheeler, a key\nquestion that arises here is: does the observed decline in moral relevance indicate that concepts within the \"supernatural\nbeing\" category are genuinely becoming less morally relevant, or might it simply reflect a decrease in the frequency of\ncertain moral words associated with this category? Clarifying this point would help disentangle genuine shifts in moral\nperception from superficial changes in linguistic frequency.\n- Wheeler, Melissa A., Melanie J. McGrath, and Nick Haslam. ‘Twentieth Century Morality: The Rise and Fall of Moral\nConcepts from 1900 to 2007’. *PLOS ONE* 14, no. 2 (27 February 2019): e0212267.\n## Universalist versus culturalist perspective\nThe authors correctly acknowledge that their framework is limited to English corpora, thus primarily reflecting the moral\nperspectives of English-speaking populations. While it is true that English serves as a global lingua franca today — carrying\nwith it cultural influences, notably American—historically, this has not always been the case. However, given that the study\nexplicitly incorporates Moral Foundations Theory (MFT) through the use of the Moral Foundations Dictionary (MFD), this\nEnglish-language limitation may not pose as significant an issue as initially stated. MFT proposes a set of fundamental\nmoral foundations that are arguably universal and somewhat language-independent, although culturally specific moral\nexpressions naturally occur. Clearly stating this alignment with MFT's assumptions regarding moral universality versus\ncultural specificity would reinforce the paper’s conceptual stance, potentially reducing concerns about the scope of linguistic\nbias in their analysis.\nThe paper already references key works on MFT, but consider adding in the Discussion (Haidt & Joseph, 2004; Graham et\nal., 2013):\n- Haidt, Jonathan; Craig Joseph (Fall 2004). \"Intuitive ethics: how innately prepared intuitions generate culturally variable\nvirtues.\" *Daedalus.* 133 (4): 55–66.\n- Graham, J.; Haidt, J.; Koleva, S.; Motyl, M.; Iyer, R.; Wojcik, S.; Ditto, P.H. (2013). \"Moral Foundations Theory: The\npragmatic validity of moral pluralism.\" *Advances in Experimental Social Psychology.* Vol. 47, pp. 55–130.\n## Reproducibility and research software engineering\nOne of the main contributions of the paper is its rigorous and scalable computational framework, which offers valuable\npotential for modeling the underlying dynamics of moralization. The authors should be commended for openly sharing their\nproject's code base. However, the current implementation appears primarily oriented toward internal research use and thus\nmight present challenges regarding reproducibility and external applicability. To substantially enhance the paper's impact,\nwe recommend making the codebase more self-contained — for example, adding clearly commented modules, a\nrequirements file, scripts for automatic data fetching, and standardized environment setup procedures. Additionally,\nrestructuring the code into more modular components would allow other researchers to easily adapt and apply the\nframework to other datasets and research contexts. This recommendation should not be taken as criticism of the current\napproach but rather as a suggestion acknowledging that research constraints often limit the resources we can dedicate to\ngood research software engineering practices.\n## Recommendation\n_Minor Revisions_", "reviewer_2": "Reviewer #2\n(Remarks to the Author)\nThis is a really fascinating paper and one that I read with a great deal of interest – being able to map the moralization of\nconcepts over time is a powerful and important technique that can offer a range of high value insights. In this sense, I felt that\npaper has the potential to add a great deal of value to the literature and to the extent that this framework is used by\nresearchers and others, the paper is likely to be highly cited.\nWith this enthusiasm on the table, I did however also have some significant questions over what the models may be\nshowing us. I should state that I do not have the technical expertise to comment on the approach to modelling or whether\nsome of the methods used are valid or not. I will leave this to other reviewers. Rather, I have focused my comments on the\nconceptual clarity of the proposed model and the demonstrations of reliability reported in this manuscript.\nMy first questions relates to how much we should be expecting to control for valence in general in these analyses. Morality is\none of the most valanced terms, and while the moral foundations dictionary is used, this has a large list of terms that\ndistinguish different foundations – a project that is not as sensitive to valence effects as this one. My concern is how we can\ndifferentiate the process of moralization from something becoming either more positive or negative in terms of its general\nvalence – for example, disease can be associated with a lot of negative associations, and not all of these need to be moral. If\nthis is the case, how do we know that the analyses provided here is specifically about morality vs. a more general valence in\nassociations?\nThis makes me wonder about the method of comparing disease (which can be highly moralized, but also is highly valenced)\nto a random set of concepts is really showing greater moral relevance, or just greater valenced associations?\nMy concerns also related to some of the validity tests reported. For example, why would a winning vs. losing side be viewed\nas morally polarized (i.e., good vs. bad) more so that just positive and negative, or having words linked to each side that is\nvalenced and associated with winning or losing respectively? Neither of which are moral concepts.\nThe analysis of price decreases and more positive moral polarity of consumer products really had me wondering whether\nthis is about moralization at all? While the associations and patterns might be there, what does it mean to view a consumer\nproduct with greater moral polarity? Does the product become more right or good? Or is it just better because it is cheaper\nand more affordable. To my thinking, all of these inferences rest very heavily on the reliability of the MF dictionary as a\nreliable indicator of moral relevance, as distinct from other sources of value association. I am not sure if it was set up to do\nwhat it is being used to do here? Even if I am wrong about this, and the authors feel it is a highly reliable indicator of moral\nrelevance per se (as opposed to a tool which distinguishes between different types of moral relevance), I would still think\nthere needs to be a clear argument for why we would expect greater moralization / moral polarity of consumer products in\nresponse to price shifts, and in a way that clearly distinguishes this from mere valence effects.\nWhile I did find some of the political predictions to be conceptually valid, some of them appears to lack face validity. For\nexample, why would drug be more moralized with democrats win? I would have thought the Republicans are more likely to\npoliticize drug use?\nI also felt that while I can see an argument for why monica may be considered moralized during particular events, again what\ndoes this mean from a theoretical perspective. Is the name monica moralized, are the acts monica engaged with moralized?\nIt would seem to be that monica the person is associated with acts that are viewed as immoral, and this to me seems more\nlike a case of moral judgement or moral condemnation rather than a process of moralizations whereby monica as a concept\n“that was previously morally neutral acquires a moral component” to draw from the definition provided. That said, I do agree\nthat moral condemnation can be a signal of moralisation, but understanding what is being moralised here seems important.\nIn the end, I really like what this paper has set out to do, but I also worry that what is reported does not always align with\nwhat we might predict, or the concept of moralisation, and may also be driven by a range of broad (and mere) valence-based\nassociations that have not been rigorously been controlled for, and perhaps even cannot easily be. That said, I would be\nvery open to any response that could provide reassurance against the concerns raised.\nBrock Bastian", "reviewer_3": "Reviewer #3\n(Remarks to the Author)\nThe authors address a key limitation in moral psychology: the lack of tools for investigating long-term, large-scale moral\nchange in the past. The paper provides a scalable, retrospective method for studying moralization using linguistic data.\nSpecifically, the manuscript presents a novel computational framework for reconstructing the historical moralization of\nconcepts years using text corpora, word associations, and graph neural networks.\nThe overall aim is strong and fills an important gap. The work is especially relevant for researchers interested in moral\nchange over time, the role of language in moral perception, and computational models grounded in psychological theory.\nStrengths\n• The paper introduces a scalable and interpretable approach to studying the historical trajectory of moral concepts—an area\nunderexplored in moral psychology. Unlike previous models relying solely on word embeddings, this framework integrates\nword associations and graph-based methods to estimate moral perception with closer psychological grounding.\n• The authors do well to situate their work in contrast to prior efforts that use word embeddings for moral sentiment analysis.\nThe critique that such models often lack psychological interpretability is important and valid. This paper attempts to bridge\nthat gap through cognitive mechanisms (word association), though more clarification is needed on how this integration\nworks in practice (see below).\n• The paper makes commendable efforts to verify its reconstructions in the absence of ground truth. The use of several\ncomparison baselines, the analysis of political figures and diseases, and the contrast with control concepts strengthen the\ncredibility of the approach.\nMajor Points / Need for Clarification\n1. Lack of Explicit Theoretical Integration:\nThe introduction of the paper claims that a principal advantage of the proposed framework is its foundation in existing\ntheories of moral psychology, emphasizing the emotional and cognitive elements that contribute to moralization. This\nassertion indicates that the framework is both data-driven and theoretically informed, providing a comprehensive view of\nmoral evolution that incorporates psychological foundations like moral emotions and cognitive reasoning. Notwithstanding\nthis assertion, the manuscript fails to furnish significant detail regarding the exact integration of these psychological theories\ninto the computational model. This represents a substantial deficiency, given the theoretical integration is regarded as a\nfundamental element of the framework's novelty. The manuscript would be enhanced by a section that clearly delineates the\nframework's components in relation to particular psychological theories. The authors must elucidate how abstract\npsychological constructs are operationalized inside their computational framework. A compelling enhancement would\ninvolve illustrating how the model's results either corroborate, expand upon, or contest established psychological theories.\nThe paper presently maintains a degree of separation between empirical data and theoretical considerations. An integrated\napproach would showcase the framework's dual efficacy in empirical innovation and theoretical integration. Overall, the\npaper should conclude with reflections on the implications of its theoretical grounding for future research. What new\nhypotheses about moralization could be tested using this framework, based on its theoretical integration? This may\ndistinguish the framework from solely empirical models and highlight its wider research significance.\n2. Comparison to Prior Work and Clarifying Contribution:\nWhile the paper rightly critiques the shortcomings of historical moral sentiment analysis using only word embeddings, it does\nnot sufficiently emphasize how its approach which still uses embeddings achieves deeper psychological grounding. The\nkey distinction appears to be the construction of association graphs, derived from historical co-occurrence patterns and\nvalidated against contemporary human association data, rather than relying solely on semantic similarity.\na. Is the main contribution that the proposed model is more cognitively plausible? If I understand the method section\ncorrectly, the authors use the cooccurrence based graphs to fine-tune the BERT embeddings to better reflect the respective\ntime-point. From the performance metrics this seems to improve prediction of moral relevance scores, but it is not fully clear\nto me how this makes the model itself cognitively plausible (given that it still relies on embeddings) and interpretable? What\nis the main difference between using embeddings (which are to some extent reflecting cooccurrence) and explicit\ncooccurrence data? In that case, would something like TF-IDF which are weighted cooccurrence data also fall under this?\nb. Therefore, I think it would be highly beneficial to clearly explain and showcase this method’s advantage. At least, the\nauthors could provide a comparative figure or schematic contrasting traditional moral (semantic) drift methods with their new\ngraph-based approach, highlighting not just performance improvements but also deeper implications for the field. They\nshould also clarify whether this yields different theoretical predictions or allows novel applications compared to embedding-\nonly models, which would further highlight its potential impact on moral psychology, i.e., clearly and specifically describe in\nthe main text what makes this approach more theoretically plausible, interpretable, etc.\n3. Limitations of Text-Based Inference:\nA potential (very general) limitation worth acknowledging is that text may not fully capture mental associations, as writing\ncan be influenced by genre, audience, editorial policies, and changing norms of expression, independent of people’s\nassociations. While this does not invalidate the approach at all, it would add balance to note that these textual patterns\nshould be interpreted as one window into historical moral perception, and what people believed or discussed in different\nsettings may not always be well represented. Again, not a major flaw of the method. In fact, the approach seems to be\ncompatible to be extended in the future to incorporate other media/data sources (e.g., cooccurrence in images/paintings),\nand methods (e.g., multimodal representations).\nMinor Points\n1. Positioning of Experimental vs. Observational Approaches:\nThe introduction critiques lab-based studies as limited because they only examine single time points. This is fair in the\ncontext of long-term moral change, but in my opinion misses an important distinction. Experimental studies aim to uncover\ncausal mechanisms behind moralization, often in real-time and with controlled manipulations. By contrast, the current\napproach is inherently observational and correlational, suited to uncovering large-scale temporal patterns that cannot be\nreplicated in the lab. It might be better to frame this as these approaches having different goals and are as such inherently\ncomplementary instead of implying insufficiency.\n2. Evaluation Metrics and Train/Test Split:\nThe authors report correlation values between model predictions and human moral association data. They do mention a\ntrain-test split in the methods, but don’t explicitly describe reported accuracy as test set performance when comparing it to\nhuman ground truth in the main text. It would be helpful to clarify here whether the reported correlations of the model\npredictions and human data are based on train or test sets.\n3. Interpreting the Moralization Rate (Figure 3a):\nFigure 3a, which plots moral relevance and rate of change, lacks a clear explanation of how rate is computed. If this is based\non annual changes in z-scored relevance, that should be stated clearly in the caption or text. Otherwise (z-score of a rate\nitself), the impression is that the changes are very small, which may be misleading. Providing a brief explanation would be\nhelpful.\n4. Temporal Confounds in Political Topic Analysis:\nIn the analysis of political topics and presidential administrations, it's unclear how temporal trends are disentangled from\npolitical cycles. For example, HIV may rise in moral relevance due to epidemiological timelines rather than election\noutcomes (of course they can be related!). Including some control for time (e.g., regression controlling for decade or year)\nwould make these findings more robust. Alternatively, the authors could acknowledge temporal trends outside of investigate\nfactors without empirically investigating them (i.e., if we wanted to analyse temporal changes in response to historical events\nwe would fit some sort of time-series model that controls for trends, autocorrelations, etc).\nConclusion\nOverall, this is a promising and well-executed paper that contributes to the study of long-term moral change using innovative\nmethods. The approach is promising for its scale and potential to complement lab-based studies of moralization. By\naddressing and clarifying the above concerns this work will better present and convey its significant contributions.", "reviewer_4": "Reviewer #4\n(Remarks to the Author)\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "Reviewer #1\n(Remarks to the Author)\nI have reviewed the authors’ revised manuscript and codebase along with their detailed response to my comments. I am\nsatisfied with the revisions, which thoughtfully address my concerns regarding cultural generalizability, software\nreproducibility, intuitive findings, attribution of moral relevance, and the interpretation of declining moral relevance over time.\nThese adjustments significantly enhance the methodological clarity and scientific robustness of an already strong paper,\nincreasing its potential impact on future research in computational analyses of moralization dynamics.\nGiven the improvements, I recommend this manuscript for publication.", "reviewer_2": "Reviewer #2\n(Remarks to the Author)\nThank you for the opportunity to review this revised manuscript. I felt that overall the authors did a solid job of addressing all\nof my concerns raised previously - which broadly fell under concerns over conflation with valence / sentiment and additional\nexplanation for the predicted / observed effects.\nIn reading this the revised manuscript I felt this was all a lot clearer now, and have no substantive concerns remaining. In my\nreading, however, I did not a few things that were not clear or slightly confusing.\n1) The third paragraph of the intrudction begins with \"To overcome this critical issue......\" where the approach being used\nappears to be described. The last paragraph then starts with \"However, a major limitation....\" where the association\nappraoch is crticised, and a pressing need to extend beyond this is noted, and it ends with \"Here, we develop a new\nframework\". I could not really follow what was happening here, and it felt as if the text was out of order? If not, then it\nprobably needs to be written more clearly.\n2) The concept of moral polarity is hard to keep clearly in mind - and while I am not opposed to its usage, I found myself\nhaving to re-read some parts to remind myself of what a high vs. low score means.\n3) This may confusion may be partly to blanme for my confusion on the explanations provided on page 9 for price shifts. It is\nstated that predictions are based on prior work that \"negative moral polarity predicts price incentives. Specifically, as\nproducts become morally negative, the amount people are willing to pay for them decreases [53], meanwhile as products\nbecome more morally\npositive people are willing to pay more for them [54, 55]\"\nBut for me at least, the following statements seem to contradict this:\n- \"In other words, products which gained positive moral associations also experienced decreases in their price change, and\nproduces (note typo here) which gained negative moral associations experienced increases in their price change\" - maybe\nis it because we are talking about change here, but I find this very hard to uppack.\n- \"Specifically, we observe that as moral polarity decreases (moral morally negative) for a product, the price of that product\ngoes up over time.\" - is it predicted that as a product gets more morally negative the price should be going down?\n- \"For instance, price increases were discussed with respect to environmental concerns and political rivalry (e.g., energy\nprices in the 2000s), and public health issues (e.g., red meat contamination in 2005).\" - this seems to infer that as products\nare associated with these moral harms, their prices are rising?\nI am sure that there is something going on with the language here - but if the data are infact saying what the words appear to\nbe saying, then this seems to be inconsistent with the predictions?\nHopefully the above can be easily clarified, but it would be helpful to make sure that the language around the direction of\neffects is clear throughout, and certainly if these effects are infact heading in the seemingly wrong direction, that this would\nneed to be considered carefully as part of any revision.", "reviewer_3": "Reviewer #3\n(Remarks to the Author)\nThe authors have thoroughly addressed my previous concerns, significantly strengthening the paper. I'm leaning toward acceptance but ask the authors to address these final points:\n1. On the Use of the Term \"Cognitive\"\nI suggest removing claims that the model is \"cognitive\" or \"cognitively plausible.\" This terminology invites unnecessary\ndebate. Static embeddings are not \"non-cognitive,\" and making them dynamic doesn't inherently make the model a cognitive\nprocess. The framework's strength is its innovative method of integrating historical text with a structure analogous to human\nword-association networks—a powerful advance that stands on its own without needing the \"cognitive\" label.\n2. On the Moral Foundations Dictionary (MFD)\nThe framework relies on an outdated version of the MFQ (and MFD) with known psychometric and sampling issues. In 2019,\nwe produced MFD 2, which is significantly more comprehensive than MFD, based on the original theorization of MFT.\nSince then, we produced more robust, cross-culturally validated version of the theory, and the Moral Foundations\nQuestionnaire 2 (MFQ2) which was developed in over 25 countries and exhibit strong psychometric properties.\nThe paper should at the very least acknowledge these limitations. I also strongly suggest running a robustness analysis\nusing MFD 2.0 (Frimer, J. A., et al., 2019. Moral foundations dictionary for linguistic analyses 2.0).\n3. On Missing Citations in Table 1\nTable 1 omits several key citations, presenting an incomplete picture of the methodology's evolution. I realize that I'm asking\nthe authors to cite more of my papers. However, I argue that providing a more accurate history is necessary, especially\nbecause the authors, correctly, make strong claims about the advantage of their proposed model over previous models. At\nthe very least, add the following foundational papers:\nTo the best of my knowledge this was the first systematic method for extracting moral language from text: Sagi, E., &\nDehghani, M. (2014). Measuring moral rhetoric in text. Social science computer review, 32(2), 132-144.\nThis is the first paper that argues for combining expert knowledge (dictionaries) and large-scale textual data. I am quite\nsurprised that the authors omit this paper: Garten, J., et al. (2018). Dictionaries and distributions: Combining expert\nknowledge and large scale textual data content analysis. Behavior research methods, 50(1), 344-361.\nOn enriching embeddings with background knowledge: Lin, Y., et al. (2018, August). Acquiring background knowledge to\nimprove moral value prediction. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and\nMining (ASONAM).\nThank you,\nMorteza Dehghani"}, "author_response": "Historical reconstruction of human moralization\nResponse letter\nAida Ramezani,1∗ Jennifer E. Stellar,2 Matthew Feinberg,3 Yang Xu,1,4\n1Department of Computer Science, University of Toronto\n2Department of Psychology, University of Toronto\n3Rotman School of Management, University of Toronto\n4Cognitive Science Program, University of Toronto\nDear Editor Dogge,\nThank you and the reviewers for the thoughtful comments and constructive feedback\non our manuscript “Historical reconstruction of human moralization.” We have carefully\nconsidered all the comments and have revised the manuscript accordingly.\nWe hope that the revised manuscript will now be suitable for publication in Nature\nCommunications.\nReviewer 1\nOur point-by-point response to Reviewer 1\nReviewer 1 comment 1\nI have reviewed the authors’ revised manuscript and codebase along with their de-\ntailed response to my comments. I am satisfied with the revisions, which thoughtfully\naddress my concerns regarding cultural generalizability, software reproducibility, intu-\nitive findings, attribution of moral relevance, and the interpretation of declining moral\nrelevance over time. These adjustments significantly enhance the methodological clar-\nity and scientific robustness of an already strong paper, increasing its potential impact\non future research in computational analyses of moralization dynamics. Given the\nimprovements, I recommend this manuscript for publication.\nRemarks on code availability:\nThe authors have now provided a detailed README that clearly outlines the\npipeline, as well as a requirements file. These additions improve the reproducibility\n1\nand scientific utility of the codebase. Furthermore, the authors have improved the\nmodularity of the code, making it easier for other researchers to adapt and reuse their\nframework.\nWethankthereviewerfortheirconstructiveandthoughtfulfeedbackonourmanuscript\nand codebase. We are pleased that the revisions have addressed their concerns.\nThank you for the opportunity to review this revised manuscript. I felt that overall the\nauthors did a solid job of addressing all of my concerns raised previously - which broadly\nfell under concerns over conflation with valence / sentiment and additional explanation for\nthe predicted / observed effects. In reading this the revised manuscript I felt this was all\na lot clearer now, and have no substantive concerns remaining. In my reading, however, I\ndid not a few things that were not clear or slightly confusing.\nOur point-by-point response to Reviewer 2\nReviewer 2 comment 1\nThethirdparagraphoftheintrudctionbeginswith”Toovercomethiscriticalissue......”\nwheretheapproachbeingusedappearstobedescribed. Thelastparagraphthenstarts\nwith ”However, a major limitation....” where the association appraoch is crticised, and\na pressing need to extend beyond this is noted, and it ends with ”Here, we develop a\nnew framework”. I could not really follow what was happening here, and it felt as if\nthe text was out of order? If not, then it probably needs to be written more clearly.\nWe thank the reviewer for raising this issue. We have revised the manuscript to im-\nprove clarity. Specifically, in the Introduction section, we first explain that prior work\nhas proposed using word association to infer human moral perception as an alternative to\npurelytext-basedcomputationaltools. Wethendiscussthekeylimitationofthisapproach,\nnamely that word association data is scarce for historical inference. To address this gap,\nwe introduce a new framework that extends moral inference via word association beyond\na single snapshot of time.\nReviewer 2 comment 2\nThe concept of moral polarity is hard to keep clearly in mind - and while I am not\nopposed to its usage, I found myself having to re-read some parts to remind myself of\nwhat a high vs. low score means.\nWe appreciate the reviewer’s thoughtful comment regarding the clarity of our con-\nstructs. Inresponse, wehaverevisedtheIntroductionsectiontoprovideaclearerdefinition\nof moral polarity with additional examples. Moreover, when discussing moral polarity in\nthe results section of the paper, we also remind the reader of what a high vs. low score\nmeans.\nReviewer 2 comment 3\nThis may confusion may be partly to blanme for my confusion on the explanations\nprovided on page 9 for price shifts. It is stated that predictions are based on prior\nwork that “negative moral polarity predicts price incentives. Specifically, as products\n3\nbecomemorallynegative, theamountpeoplearewillingtopayforthemdecreases[53],\nmeanwhile as products become more morally positive people are willing to pay more\nfor them [54, 55]” But for me at least, the following statements seem to contradict\nthis:\n- “In other words, products which gained positive moral associations also expe-\nrienced decreases in their price change, and produces (note typo here) which gained\nnegative moral associations experienced increases in their price change” - maybe is it\nbecause we are talking about change here, but I find this very hard to uppack.\n-“Specifically,weobservethatasmoralpolaritydecreases(moralmorallynegative)\nfor a product, the price of that product goes up over time.” - is it predicted that as a\nproduct gets more morally negative the price should be going down?\n- “For instance, price increases were discussed with respect to environmental con-\ncerns and political rivalry (e.g., energy prices in the 2000s), and public health issues\n(e.g., red meat contamination in 2005).” - this seems to infer that as products are\nassociated with these moral harms, their prices are rising?\nI am sure that there is something going on with the language here - but if the data\nareinfactsayingwhatthewordsappeartobesaying,thenthisseemstobeinconsistent\nwith the predictions?\nHopefully the above can be easily clarified, but it would be helpful to make sure\nthat the language around the direction of effects is clear throughout, and certainly if\nthese effects are infact heading in the seemingly wrong direction, that this would need\nto be considered carefully as part of any revision.\nWe thank the reviewer for highlighting this issue. To clarify, our results indicate a\nconsistent negative relationship between changes in moral polarity and changes in product\nprices. Specifically, as a product’s moral polarity score goes down (i.e., it is viewed more\nmorally negative with respect to the last year’s score), its price goes up, and conversely,\nwhen a product’s moral polarity score goes up (i.e., it is viewed more morally positive\nwith respect to the last year’s score) its price goes down. For example, beef experienced\na substantial price increase from 2004 to 2005 relative to other consecutive years, and its\nmoralpolarityscoreoverthesameperiodshowedacorrespondingdecline,reflectingamore\nnegative moral perception.\nWhile prior work cited in our manuscript examined the directional effect of moral per-\nception on willingness to pay, our study does not test causal directionality due to limited\nlong-term price data for all products. Additionally, unlike prior work that studied will-\ningness to pay, we examine actual product prices, which may or may not directly reflect\nconsumers’ willingness to pay for various products.\nIn the reported examples, products such as beef and energy became more expensive in\nyears when they were discussed in the context of political, social, or environmental issues.\nThis pattern may reflect the influence of a common external factor (e.g., crises) affecting\nboth price and moral perception, or alternatively, that price fluctuations contributed to\n4\nshifts in moral perception.\nWe have revised the manuscript section Moral change under economic and political\nshiftstoensurethatthelanguagedescribingthedirectionoftheseobservationsisconsistent\nthroughout.\nThe authors have thoroughly addressed my previous concerns, significantly strengthening\nthe paper. I’m leaning toward acceptance but ask the authors to address these final points:\nOur point-by-point response to Reviewer 3\nReviewer 3 comment 1\nOn the Use of the Term “Cognitive”\nI suggest removing claims that the model is “cognitive” or “cognitively plausi-\nble.” This terminology invites unnecessary\ndebate. Static embeddings are not “non-\ncognitive,” and making them dynamic doesn’t inherently make the model a cognitive\nprocess. The framework’s strength is its innovative method of integrating historical\ntext with a structure analogous to human word-association networks—a powerful ad-\nvance that stands on its own without needing the “cognitive” label.\nWe thank the reviewer for raising this point, and their recognition of our framework\ncontributions. We have revised the manuscript throughout to address this concern.\nReviewer 3 comment 2\nOn the Moral Foundations Dictionary (MFD)\nThe framework relies on an outdated version of the MFQ (and MFD) with known\npsychometric and sampling issues. In 2019, we produced MFD 2, which is significantly\nmore comprehensive than MFD, based on the original theorization of MFT.\nSince then, we produced more robust, cross-culturally validated version of the\ntheory, and the Moral Foundations Questionnaire 2 (MFQ2) which was developed in\nover 25 countries and exhibit strong psychometric properties.\nThe paper should at the very least acknowledge these limitations. I also strongly\nsuggestrunningarobustnessanalysisusingMFD2.0(Frimer,J.A.,etal.,2019. Moral\nfoundations dictionary for linguistic analyses 2.0).\nWe thank the reviewer for this thoughtful comment. We agree with the reviewer and\nconfirm that we used MFD version 2.0 from Frimer et al. consistently throughout all\nanalyses. In addition, we have included the “General” category from version 1.0 to capture\nthe effect of morally relevant words that are not linked to any specific moral foundation.\nThis information is provided in Computation of moral scores based on word association\ndata subsection under Methods.\nReviewer 3 comment 3\nOn Missing Citations in Table 1\nTable1omitsseveralkeycitations,presentinganincompletepictureofthemethod-\nology’s evolution. I realize that I’m asking the authors to cite more of my papers.\ncausetheauthors, correctly, makestrongclaimsabouttheadvantageoftheirproposed\nmodel over previous models. At the very least, add the following foundational papers:\nTo the best of my knowledge this was the first systematic method for extracting\nmoral language from text: Sagi, E., & Dehghani, M. (2014). Measuring moral rhetoric\nin text. Social science computer review, 32(2), 132-144.\nThis is the first paper that argues for combining expert knowledge (dictionaries)\nand large-scale textual data. I am quite surprised that the authors omit this paper:\nGarten, J., et al. (2018). Dictionaries and distributions: Combining expert knowledge\nand large scale textual data content analysis. Behavior research methods, 50(1), 344-\n361.\nOn enriching embeddings with background knowledge: Lin, Y., et al. (2018, Au-\ngust). Acquiring background knowledge to improve moral value prediction. In 2018\nIEEE/ACM International Conference on Advances in Social Networks Analysis and\nMining (ASONAM).\nWe thank the reviewer for suggesting these references. They are highly relevant to our\nwork. We have revised Table 1 to provide a more comprehensive overview of prior research\nand updated the Introduction to reflect these additions. Table 1 currently includes citation\nfrom the following work:\n• Sagi, E. & Dehghani, M. Measuring moral rhetoric in text. Social Science Computer\nReview. 32, 132–144 (2014)\n• Wheeler, M. A., McGrath, M. J. & Haslam, N. Twentieth century morality: The rise\nand fall of moral concepts from 1900 to 2007. PLoS ONE 14 (2019).\n• Garten, J. et al. Dictionaries and distributions: Combining expert knowledge and\nlarge scale textual data content analysis: Distributed dictionary representation. Be-\nhavior Research Methods. 50, 344–361 (2018).\n• Xie, J. Y., Ferreira Pinto Junior, R., Hirst, G. & Xu, Y. Text-based inference of\nmoral sentiment change. Proceedings of the 2019 Conference on Empirical Methods\nin Natural Language Processing.\n• Lin, Y. et al. Acquiring background knowledge to improve moral value prediction.\nand Mining (ASONAM)\n• Schramowski, P., Turan, C., Andersen, N., Rothkopf, C. A. & Kersting, K. Large\npre-trained language models contain human-like biases of what is right and wrong to\ndo. Nature Machine Intelligence 4, 258–268 (2022).\n• Ramezani, A. & Xu, Y. Moral association graph: A cognitive model for automated\nmoral inference. Topics in Cognitive Science.\n7"}, {"round": 3, "reviewer_comments": {"reviewer_2": "Reviewer #2\n(Remarks to the Author)\nThank you for addressing my remaining concerns. This is all much clearer now. I would perhaps suggest just one more\nminor change. It seems clear that your findings for moral polarity and price are inconsistent and in fact opposite to the work\nwhich motivated your research question. Where past work shows people are less willing to pay for morally negative items,\nyours show moral negativity is associated with higher prices. I cannot see that you have explicitly noted this inconsistency. I\nalso think the third variable explanation (i.e., crises) limits the implication of this finding, suggesting the reported relationship\nmay not be meaningful at all, but simply conflated due to the existence of some background factor independently shaping\nboth variables. I feel that in the context of the rest of the manuscripts findings, reporting these data is still okay - but a much\nclearer acknowledgement that the finding is a) inconsistent with past work, and therefore could not be predicted a priori, and\nb) potentially suffers from a third variable problem potentially rendering the relationship itself meaningless is important to do\nexplicitly. It would also be good if there was some attempt to explain the relationship in a meaningful way.", "reviewer_3": "Reviewer #3\n(Remarks to the Author)\nAll my previous concerns have been address. This is a wonderful paper and it is ready for publication.\nMorteza Dehghani"}, "author_response": "Historical reconstruction of human moralization\nResponse letter\nAida Ramezani,1∗ Jennifer E. Stellar,2 Matthew Feinberg,3 Yang Xu,1,4\n1Department of Computer Science, University of Toronto\n2Department of Psychology, University of Toronto\n3Rotman School of Management, University of Toronto\n4Cognitive Science Program, University of Toronto\nReviewer 2\nOur point-by-point response to Reviewer 2\nReviewer 2 comment 1\nThank you for addressing my remaining concerns. This is all much clearer now. I\nwould perhaps suggest just one more minor change. It seems clear that your findings\nfor moral polarity and price are inconsistent and in fact opposite to the work which\nmotivated your research question. Where past work shows people are less willing to\npay for morally negative items, yours show moral negativity is associated with higher\nprices. I cannot see that you have explicitly noted this inconsistency. I also think the\nthird variable explanation (i.e., crises) limits the implication of this finding, suggesting\nthe reported relationship may not be meaningful at all, but simply conflated due to\nthe existence of some background factor independently shaping both variables. I feel\nthat in the context of the rest of the manuscripts findings, reporting these data is still\nokay - but a much clearer acknowledgement that the finding is a) inconsistent with\npast work, and therefore could not be predicted a priori, and b) potentially suffers\nfrom a third variable problem potentially rendering the relationship itself meaningless\n1\nis important to do explicitly. It would also be good if there was some attempt to\nexplain the relationship in a meaningful way.\nWethankthereviewerforraisingthisimportantissue. Wehaverevisedthemanuscript\nto explicitly acknowledge that our finding contrasts with prior research showing people are\nless willing to pay for morally negative items. We explicitly discuss the causal ambiguity\nof our correlational finding, noting that it could reflect multiple mechanisms, including\ncommonexternalfactorsthatindependentlyaffectbothmoralityandprice, asthereviewer\nnoted. WeappliedthisrevisioninSectionMoral change under economic and political shifts\non page 8 of the revised manuscript.\nWe believe these revisions address the reviewer’s concerns by providing explicit ac-\nknowledgment of both the inconsistency with prior work and the interpretive limitations\nof this finding.\n2"}]

Decision letter