A Survey on Modelling Morality for Text Analysis (2024)

Abstract: In this survey, we provide a systematic review of recent work on modelling morality in text, an area of research that has garnered increasing attention in recent years. Our survey is motivated by the importance of modelling decisions on the created resources, the models trained on these resources and the analyses that result from the models’ predictions. We review work at the interface of NLP, Computational Social Science and Psychology and give an overview of the different goals and research questions addressed in the papers, their underlying theoretical backgrounds and the methods that have been applied to pursue these goals. We then identify and discuss challenges and research gaps, such as the lack of a theoretical framework underlying the operationalisation of morality in text, the low IAA reported for manyhuman-annotated resulting resources and the lack of validation of newly proposed resources and analyses.

## introduction with the rise of large language models, research goals in nlp have also become more ambitious, tackling new challenges like the prediction of complex psychological constructs. a case in point is the modelling of morality in text, a task that requires a deep and comprehensive understanding of natural language. more and more studies at the interface of nlp and computational social science (css) have addressed this task, based on different theoretical frameworks, and a variety of methods have been applied to predict moral values from text. these studies aim at modelling the moral sentiment that a person or group holds toward a certain target in order to investigate research questions from the political or social sciences. others model morality in the context of ai applications, e.g., to study the inherent moral biases learned by language models. many resources have been created, but are * equal contribution between the first three authors. modelling morality using nlp techniques has also been at the center of recent events such as workshops or coding competitions. for example, @xcite identify human values behind arguments in a semeval shared task (val-ueeval'23) foot_0 while the mp foot_1 workshop 2 at neurips 2023 looks at the application of theories from moral philosophy and psychology to ai practices, demonstrating the growing interest in exploring morality with computational methods in interdisciplinary settings. this survey aims at providing a systematic overview of recent work on modelling morality in text, the resources that are available, and the methods that have been applied. we argue that the operationalisation of morality has an immense impact on (i) the annotated resources that are created, (ii) the models that are trained, based on these resources, (iii) the predictions obtained from these models, and (iv) the analyses that result from the predictions. our survey therefore focusses on the theoretical basis and modelling part, as well as on the validation of the concept. this is not a survey on ethics in ai, which was the main focus of @xcite . motivated by the confusion regarding concepts from philosophical ethics in nlp research, @xcite focus on concepts from philosophy and analyse literature on moral nlp with respect to their philosophical foundation and terminology. we, instead, focus on the theoretical modelling and operationalisation of morality for text analysis and the challenges that arise for applications in css and cultural analytics. these different backgrounds are also reflected in the paper selection method and the set of surveyed papers, which we briefly discuss in the appendix, §a.2. the survey is structured as follows. we first describe our survey methodology ( §2) and outline the research objectives and background of the papers included in the survey ( §3). then we describe different operationalisations of morality in text ( §4) and their impact on applications in the social sciences ( §5). we discuss trends and research gaps in section 6 before we conclude and outline some recommendations ( §7). the supplementary materials together with our code can be found in our github repository: https: //github.com/umanlp/survey_morality . ## survey methodology in the following, we briefly describe our methodology for selecting and reviewing the papers for this survey. our paper selection method is inspired by @xcite and also follows recommendations from @xcite . the different steps are summarized in figure 1 , more details can be found in the appendix. screening and reviewing (see §a.1.4 to §a.1.6) the set of papers resulting from paper sampling and snowballing is then screened following six selection criteria (cf. §a.1.4) that must all be satisfied for a paper to be included in the survey. after screening, 135 papers are kept and distributed amongst the authors for reviewing. to increase consistency across reviewers, we use a survey form with an accompanying codebook for reviewing, both refined during an initial pilot study based on eight selected papers. after the reviewing process, we identify 4 demo papers, 1 shared task paper and 14 papers that, according to our selection criteria, were not included in the survey. this leaves us with 116 papers as the basis of the analyses presented in the remainder of this work. figure 2 shows the distribution of publication years for the papers, demonstrating the growing interest in the field (also see ## survey overview to get an overview of the work included in the survey, we first classify papers according to their main research objectives, as listed below (note that a paper can have more than one objective). the majority of the papers model morality to analyse the moral values and stances held by an individual or group, and to investigate moral framing (87 papers, see according to their main contributions, the papers can be broadly categorised into experimental papers, analysis papers and resource papers. 3 experimental papers this category includes work that focusses on the development and evaluation of methods for the identification of moral language in text. with 81 papers, it is the largest of the three categories. regarding the machine learning methods used, we observe the following trends (see analysis papers the second largest category includes 65 papers that present an analysis based on the application of nlp methods to predict moral values in text. papers in this category address re-3 note that the contributions are not mutually exclusive. method # papers fine-tuned transformers 29 dictionary-/rule-based 25 feature-based ml 18 llms (zero-/few-shot) 14 nns with static embeddings 8 semi-supervised ml 8 logic-based ml 6 unsupervised ml 10 reinforcement learning 2 in summary, the majority of the papers in our survey use text-based models of morality to study moral sentiment and moral framing, typically applied to research questions from the css. given the strong focus on real-world applications, the question arises as to the validity of the various approaches used to model morality in text. we address this issue in the next section. ## operationalisations of morality next, we study how the different papers in the survey operationalise the concept of morality. we first give an overview over the task of moral value prediction. then we look at the theoretical frameworks that have been employed, and turn to the question on which level morality is investigated in the papers. finally, we discuss the issue of underspecification in modelling morality and its negative impact on the reliability and validity of the analyses. ## applications of moral value prediction we now focus on the application of computational methods for moral value prediction in real-world analyses. our findings show that the thoroughness of the research methodology leaves something to be desired. only few of the 65 analysis papers in the survey explicitly state their research questions (13 papers), and an equally small number of papers (10) formulates hypotheses and tests for statistical significance, while the vast majority of the analysis papers use data exploration or visualisations to support their findings. as dictionaries were among the most often applied method for predicting moral values in the analysis papers (also see ## trends, research gaps and recommendations after discussing issues regarding the modelling of morality in text and limitations of current applications to research questions in the computational social sciences, we now outline some trends emerging from the survey, as well as research gaps and challenges that we would like to see addressed in future work. resources for languages other than english while several corpora and dictionaries are available for the analysis of morality in english text, only few resources exist for other languages. this is in line with the findings in @xcite . out of the 18 studies that work with languages other than english, only 6 release an annotated dataset for a new language. 5 papers use dictionary-based approaches, typically based on translations of one or more of the english mfds without proper validation for the new language. a notable exception is cheng and zhang (2023) who introduce new moral foundations for chinese and also test for validity (see §4.2). the remaining papers either use surveyderived measures of morality, annotate stances on moral topics, or present an annotation tool. thus, the creation of annotated datasets and tools for languages other than english remains an important research objective. modelling grounded in theory a high percentage of the papers included in the survey (20%) are not based on any theoretical framework. we argue that grounding models of morality in theory has several advantages. first, it provides a link to previous research and can thus inform our research design and modelling decisions. second, a sound and well-defined theoretical framework can help address the problem of underspecification in modelling. finally, the theory can provide us with testable hypotheses and, vice versa, our results can help improve theory development. we therefore recommend researchers that aim at building new resources to choose an appropriate framework as the theoretical basis for their work. underspecification another problem we found concerns the creation of annotated resources as training data. as mentioned above, annotation procedures and guidelines lack (i) transparency and reproducibility, as only half of the survey papers release their guidelines, and (ii) specificity, as the guidelines are often shorter than one page. we therefore recommend the creation and publication of more specific and detailed guidelines for the annotation of morality that provide coders with sufficient information on how to deal with the already challenging task. in section 4.5, we argued that it is crucial for the annotation of morality to also encode the perspective, not only to reduce inconsistencies in the annotations, but also to make the resulting resources more useful for different types of analyses. we therefore recommend annotating morality at the level of frames and roles, to explicitly capture this information. representativity, reliability, validity we highlight the importance of ensuring the representativity of the research data for the population relevant for the respective research questions, especially in comparative studies. to increase the reliability and validity of the analyses, we advise researchers to formulate their research questions, form hypotheses and test for statistical significance. in addition, more work is needed on how to test for different types of validity concerning the construct of morality. ## conclusion in the survey, we present a systematic review of work on modelling morality in text. we highlight problems that need to be addressed, one of them being the lack of resources for languages other than english. another issue concerns the low iaa for the annotation of morality in text. we argue that one reason for this, besides the inherent subjectivity of the task, results from underspecified task instructions, witnessed by a) very short or unavailable guidelines and b) the attempt to code morality on the sentence or document level, making it hard for the annotators to know which aspects of the text they are supposed to code. to address this issue, we recommend annotating morality at the level of frames, to make it clear what is being annotated and from whose perspective. another problem we found is the lack of validation of both the resources and the analyses. while many papers use nlp methods to investigate research questions in css, only few studies formulate hypotheses or use significance testing. equally important is the representativeness of the data, especially in comparative studies, and the extent to which it is justified to replace carefully validated methods such as questionnaires with automated dictionary-based text analysis procedures. this should be investigated in future studies. with the paper, we release a checklist that addresses crucial design decisions for text-based analysis that we hope will help researchers to identify some of these issues.