An unsupervised framework for tracing textual sources of moral change (2021)

Abstract: Morality plays an important role in social well-being, but people’s moral perception is not stable and changes over time. Recent advances in natural language processing have shown that text is an effective medium for informing moral change, but no attempt has been made to quantify the origins of these changes. We present a novel unsupervised framework for tracing textual sources of moral change toward entities through time. We characterize moral change with probabilistic topical distributions and infer the source text that exerts prominent influence on the moral time course. We evaluate our framework on a diverse set of data ranging from social media to news articles. We show that our framework not only captures fine-grained human moral judgments, but also identifies coherent source topics of moral change triggered by historical events. We apply our methodology to analyze the news in the COVID-19 pandemic and demonstrate its utility in identifying sources of moral change in high-impact and real-time social events.

## introduction from ancient greek scholars to philosophers of the past centuries, morality has been a subject of central importance in human history @xcite @xcite @xcite . despite this importance, people's morals are not static but change over time @xcite . recent advances in natural language processing (nlp) have shown that text can inform moral sentiment and its change over time (e.g., how slavery was increasingly perceived to be morally wrong) @xcite . however, critically under-explored are the origins of these changes. we present a framework for tracing textual sources of moral change that requires minimal human intervention or supervision. the study of moral sentiment is a prominent subject in social psychology @xcite @xcite @xcite , and the advent of moral foundations theory @xcite has provided an impetus for text-based analysis of moral sentiment in natural language processing. existing studies range from moral sentiment classification to temporal inference of moral sentiment change (e.g., @xcite @xcite @xcite . the problem we focus on here is how moral perception toward entities (e.g., political leaders) varies through time, and whether textual analysis can help extract the sources of this variation. for instance, an entity like bill clinton could be applauded for charity at one time but deprecated for a sex scandal at another time. similarly, moral sentiment toward a more general entity like policemen could undergo a negative shift due to acts on racial discrimination. existing methods for moral sentiment detection typically take an aggregate approach and do not focus on analyzing moral sentiment of entities @xcite @xcite . here, we develop a methodology to identify textual sources that give rise to moral sentiment change toward an entity. our work takes a similar approach to detecting sources of gender bias in text by locating a set of documents that influence gender bias in word embeddings @xcite . we propose a probabilistic unsupervised framework informed by both textual inference of moral sentiment and dynamic topic model @xcite . capturing events as topic distributions, we approach this problem by decomposing textual mentions of an entity into topics and quantifying the contributions of different topics toward moral sentiment of an entity. we attribute the origins of moral change as topics that contribute saliently to changes in the time course of moral sentiment. we compare this approach with classic work on influence function @xcite , which has been used to quantify the effect of samples in statistical estimation (see also @xcite . figure 1 illustrates our framework. figure 1a shows the generative process in our topic-based approach. given an entity (e.g., donald trump) and its mentions in a set of documents (e.g., tweets or news articles), we wish to infer the most salient source topic(s) that gave rise to changes in the moral sentiment time course about that entity. here as an illustration the moral sentiment toward donald trump is analyzed through a set of news articles. each article includes mentions of this entity, specified as topical distributions that contribute toward the perceived moral sentiment of the entity (see figure 1b ). as moral sentiment of the entity changes over time, our framework uses probabilistic inference jointly with change point analysis to extract the most salient topic and its relevant source documents that underlie these changes. we show how our approach predicts fine-grained human moral judgment variation across topics and identifies influential and coherent text as the sources of moral change for both historical and modern events. ## related work on textual inference of moral sentiment in nlp the development of moral foundations theory (mft) jointly with moral foundations dictionary (mfd) @xcite has propelled recent research in the natural language processing community to explore automated textual inference of moral sentiment. mft sought to explain the cultural variation in morality and moral concerns along five or six moral foundations, each organized in terms of the polarities virtue (+) and vice ( ). the computational methods using mft tend to rely on supervised approaches to predicting the moral sentiment reflected in text @xcite @xcite . other related work has characterized moral biases in language models @xcite @xcite , and contributed new datasets for tasks such as automatic ethical judgment and inference of sociomoral norms @xcite @xcite . existing work has also studied moral sentiment change over time @xcite showing how word embeddings capture hidden moral biases underlying different concepts (e.g., slavery) in history. this model uses mfd words as seeds and a hierarchical framework to capture moral change in three tiers: moral relevance, moral polarity, and fine-grained moral foundations. 1 here we go beyond this line of research by developing an unsupervised framework that automatically identifies sources of moral change toward entities in text. ## methodology we formulate textual source tracing of moral change as a probabilistic inference problem. this model allows us to identify the sources of change at the topic level (source topic), as well as retrieve a set of related documents (source documents) underlying the detected moral change. to do so, we need to quantify 1) the moral time course of an entity based on textual input, and 2) the influence of topics on the changes in moral time course. 2 quantification of moral time course. we estimate the moral sentiment along moral dimension m for entity e at time point t as follows: here d e,t is the set of documents (indexed by d) at time point t that contain entity e at least once. 3 for example, d e,t can be all the documents in our corpus that are published in @xmath0, and include a mention of entity @xmath1. moral dimension m can be moral relevance, moral polarity, or one of the moral foundations in mft. to construct a vector representation for a document, we exclude all the sentences in d that do not include any mentions of entity e. after lemmatizing the rest of the document using spacy english model, we remove 1) function words, 2) entity e and its mentions, and 3) words that are classified as morally irrelevant by the centroid model following @xcite . we then derive the vector representation of document d by taking an average of the semantic vector representations (i.e., word embeddings) of the remaining words: here v d is the vector representation of document d, and v w is the vector representation of lemma w. to estimate p e (m|d), we use the centroid model in @xcite . this model estimates this probability by comparing the similarity of an input vector (i.e., v d ) to each of its centroids. the centroids of this model in the moral relevance tier are the average word embeddings of mfd words and a set of morally neutral words. for moral polarity, the centroids are based on moral virtue and vice words from mfd. for the fine-grained tier, there are 10 centroids, each being the average word embeddings of the words in a moral foundation. ## experiments and results we evaluate and apply our framework in three diverse and real-world settings. ## discussion and conclusion we have presented an unsupervised framework that uses topical information to infer textual sources of moral change for entities. our work extends existing nlp methods for moral change inference by identifying the origins of moral change over time. with evaluations on a diverse set of data, we show that our method captures both the fine-grained human moral judgments and coherent source text of moral change relevant to social events. our approach differs from work on general sentiment inference, partly because moral sentiment has a more fine-grained and hierarchical structure that involves inference at three different tiers, e.g., moral relevance, moral polarity (vice vs. virtue), and moral foundations. previous work has evaluated rigorously models that capture this 3-tier moral hierarchy @xcite . our framework builds on this study by characterizing the textual source of moral change at each of the three tiers. although moral polarity can overlap with general sentiments such as good and bad, our framework captures moral sentiment beyond this dichotomous dimension. for instance, an increase in the moral relevance of an entity can be driven by an increase in moral authority, which may or may not involve any positive or negative sentiment (see appendix c for examples). in this respect, moral sentiment captured by our framework can be dissociated with sentiment portrayed in the traditional nlp literature. our work makes minimal claims about the causes of moral change. our focus here is to identify salient topics as the source of moral sentiment change. this topical information can be a proxy to world events that trigger changes in moral perception toward an entity. identifying the causes of moral change beyond textual sources studied here can be an exciting yet challenging direction. our framework also offers opportunities for further exploration of entity-based moral sentiment change. future work may explore how different media platforms vary in the moral sentiments that they convey towards entities (e.g., public figures) and the sources of this variation.