Exploring Morality in Argumentation (2020)

Abstract: Sentiment and stance are two important concepts for the analysis of arguments. We propose to add another perspective to the analysis, namely moral sentiment. We argue that moral values are crucial for ideological debates and can thus add useful information for argument mining. In the paper, we present different models for automatically predicting moral sentiment in debates and evaluate them on a manually annotated testset. We then apply our models to investigate how moral values in arguments relate to argument quality, stance and audience reactions.

## introduction argumentation mining is a new research field that is closely related to the subfields of stance detection and sentiment analysis @xcite , since "every argument carries a stance towards its topic, often expressed with sentiment". 1 in addition to stance and sentiment, there is another dimension that can play an important role in debates, namely moral beliefs. a debater's moral beliefs go beyond stance and can be expressed with varying sentiment. they play an important role in ideological debates and cannot be resolved by simply comparing facts but often involve a battle of ideas and a clash of different belief systems. consider the following arguments on whether or not gay marriage should be legal. 2 (1) the institution of marriage has traditionally been defined as being between a man and a woman. both arguments are based on moral belief systems. the first argument refers to moral values that promote respect for tradition, while the second focuses on fairness and equal rights. arguments that express an opposite stance towards the topic usually differ concerning their moral framing. on the other hand, we observe that arguments expressing a similar stance towards a certain topic may still differ with regard to how the argument is framed, as illustrated in example (3) and (4) below. while (3) opposes the legalization of prostitution because it is considered as a harmful form of oppression targeting women, example (4) depicts prostitution as increasing the danger of diseases and contamination. this makes moral framing an interesting ingredient for argument mining. in the paper, we argue that identifying moral values in debates has the potential to support argument analysis and to help with different subtasks related to argument mining. being able to distinguish between arguments with similar stance and sentiment but framed according to different moral categories can help to identify new arguments and can improve camp detection, thus supporting more fine-grained modeling of debaters beyond stance. furthermore, moral framing is of particular interest for the analysis of political debates @xcite . in practice, however, predicting moral sentiment from text poses several challenges. first, morality is a fuzzy concept, and it is difficult to find an operationalization that turns it into measurable data. moral sentiment is often expressed implicitly and thus hard to detect, based merely on the presence of lexical cues. in addition, human coders might be biased by their own belief systems, which casts doubt on the validity of the annotations used to train or evaluate automatic systems. in the paper, we present work in progress where we evaluate different models for the prediction of moral framing in text on two datasets and assess the benefits of these predictions for the analysis of arguments. based on three datasets with argumentative text, we investigate whether we can find correlations between moral values and different aspects of argumentation, such as argument quality, stance, or audience approval. we are interested in the following research questions: rq1: do debaters that produce high-quality arguments make more or less frequent use of moral framing? rq2: is moral framing more strongly related to a positive or negative stance? rq3: can we find a positive correlation between the audiences' approval and the use of moral frames? our main contributions are the following: (i) we augment the argquality corpus of @xcite with annotations for moral values, as a first test set for the evaluation of moral sentiment in argumentation; (ii) we evaluate two methods for the prediction of moral sentiment on the new dataset; (iii) we present a correlation study investigating the relation between moral framing and argument quality, stance, and audience reactions. the paper is structured as follows. we first review work on quantifying moral sentiment in text ( §2). in §3, we describe the annotation of our test set and present different approaches to the automatic detection of moral sentiment in debates. then we discuss our correlation analysis ( §4), and in §6 we summarise our results and conclude. ## related work as an operationalisation for the concept of moral sentiment, we refer to moral foundations theory (mft) @xcite . mft has its roots in social and cultural psychology and assumes the existence of innate and universally available psychological systems that build the foundations of intuitive ethics. these foundations are augmented by culture-specific constructs of virtues and backed up by personal narratives "that people construct to make sense of their values and beliefs" @xcite , and are also reinforced by institutional environments. mft assumes that all moral issues can be described along the following dimensions: care-harm, fairness-cheating, loyalty-betrayal, authority-subversion, and purity-degradation. 3 dictionary-based approaches to mft the first version of the moral foundations dictionary (mfd) was presented by @xcite and has been used for a content analysis of christian sermons held in liberal and conservative congregations. each of the five moral foundations listed above has been further split into a vice and a virtue subcategory, reflecting the positive and negative ends of each dimension. examples are peace*, protect*, compassion* for care virtue and suffer*, crush*, killer* for care vice . the mfd includes, on average, 32 words per moral subcategory. @xcite presents a new version of the mfd with more entries per mf subcategory, selected according to prototypicality estimates for each mf, based on cosine similarity for word2vec embeddings for each item in the dictionary. while the authors admit that the construct validity of the mfd 2.0 is not better than for the original mfd, they recommend the use of the mfd 2.0 due to its improved coverage. other work on expanding the mfd includes @xcite who increase the original size of the dictionary to over 4,600 lexical items, using a quality controlled, human in the loop process. 4 the mf dictionary has been used in several studies in the political and social sciences, psychology, and related fields @xcite @xcite . dictionary-based approaches to measuring moral values in text, however, have severe shortcomings. they can neither account for unknown words or the different meanings a word can take, nor do they consider that shifter words and negation can change the polarity of an expression. in addition, we expect that moral vocabulary might vary considerably, depending on the speaker's age and other geopolitical, social, and cultural variables. @xcite address the coverage problem of dictionary-based approaches by replacing the terms in the mf dictionary with their averaged vector representations in distributional space. they show that predicting moral foundations based on the cosine similarity of the words in a text to the distributional representations outperforms a naive method that predicts mf based on word counts. machine learning-based approaches recent work has applied the framework of mft to research questions in the social and political sciences @xcite @xcite , replacing dictionary-based counts with more sophisticated methods. @xcite model moral framing in politicians' tweets, using probabilistic soft logic (bach et al., 2013). lin et al. (2018) improve the prediction of moral foundations by acquiring additional background knowledge from wikipedia, using information extraction techniques such as entity linking and cross-document knowledge propagation. xie et al. (2019) study the change of moral sentiment in longitudinal data, presenting a parameter-free model that predicts moral sentiment on three different levels: (i) moral relevance, (ii) moral polarity, and (iii) the ten moral subclasses of the mfd encoding the virtue/vice dimension for each mf. finally, resapour et al. (2019) show that using dictionary counts for moral sentiment as features in a supervised classification setup can increase results for stance detection. ## correlation studies to study the impact of moral framing in argumentation, we investigate the correlation between moral sentiment and other properties of argumentative text, namely argument quality, stance, and audience reactions. for this, we use the multilabel-bert model that yielded the best results on both datasets. ## discussion a crucial issue for using moral values for argumentation analysis concerns the reliability of the (manual and automatic) annotations. before we can reliably use moral values for the analysis of arguments, we need to ensure the quality of the annotations, as the low inter-annotator agreement for mf annotation casts doubt on the validity of the findings. while we expect that more extensive training and more detailed guidelines will increase iaa for human annotation at least slightly, we still think that due to the fuzziness of the concept of morality, high agreement scores are not very probable. thus, we would like to propose a different approach to the annotation of moral foundations where we ground the annotations in lexical semantics. this approach has already been shown to improve iaa for a similarly difficult annotation task, namely the annotation of causal language @xcite . the authors created a lexical resource for terms that can trigger causality in text and instructed annotators to disambiguate instances of those terms in context, showing that their modularized, dictionary-based approach yields substantially increased iaa scores. inspired by their work, we propose to anchor mf annotations in lexical semantics, using an expanded version of the mfd as seed terms. the annotators will then be presented with instances of these terms and instructed to disambiguate them in context, and also to annotate a small, predefined set of semantic roles, such as betrayer, harm doer, victim, and so on. setting up the annotation of moral values as a frame semantic annotation task has several advantages. first, the addition of semantic roles would make the annotations more informative by encoding the core participants of moral arguments, i.e., the target of the moral evaluation and the affected party. second, anchoring the annotations in lexical semantics would make it easier to provide the annotators with precise guidelines. this might not only increase the consistency of the annotations but might also help to control for annotator bias. an open question, however, concerns coverage as it is not yet clear whether this approach would miss too many relevant expressions of moral values, given that it only captures explicitly stated moral evaluation but not implicit judgments. whether the merits of our proposal outweigh its drawbacks needs to be explored in future work. being able to predict moral values in text reliably can open up new research avenues in argumentation. e.g., recent work in psychology has shown that moral values play an important role in debates on political and social issues @xcite @xcite . for example, @xcite have shown that debates on environmental issues are often framed in terms of moral values such as care-harm, a moral foundation that is at the core of liberal belief systems, while conservatives, in contrast, seem to value all five mfs more similarly @xcite . this often results in highly polarized discussions, and @xcite argue that reframing such issues in terms of moral values that explicitly address the opponents' belief system might have the potential to depolarize controversial debates and improve understanding between the camps by addressing the "moral empathy gap" (see @xcite and references therein). ## conclusion and future work in the paper, we evaluated different models for predicting moral sentiment in debates, based on moral foundations theory. we then used our models to predict moral values in three argumentation datasets. we investigated whether we could find a correlation between morality and (i) argument quality, (ii) stance, and (iii) audience reactions for political speeches. we found weak but significant correlations between general morality and argument quality in the argquality data and a consistent positive correlation between moral sentiment and audience approval in corps as well as a negative correlation for moral sentiment and laughter. however, our study has several limitations that need to be addressed. first, the annotation experiment has been conducted by two annotators only, not allowing us to retrieve more reliable labels using the wisdom of the crowd. also, the size of the test set is rather small, thus questioning the reliability of the results. another problem is the low accuracy of the classifiers for the prediction of moral values. while results were substantially higher than the random baseline and an mfd-based baseline, we still expect a considerable amount of noise in the classifiers' predictions, which might impact the results of the correlation study. it is conceivable that cleaner predictions might increase the effect size of the observed correlations, which would be consistent with the slightly larger correlation coefficients found for the human annotations (hu). this, however, still needs to be confirmed. the next steps should include the creation of larger test sets where the annotations have been validated by more than two annotators as well as the evaluation of semantically grounded approaches to coding moral values, to assess their reliability and validity. another important task is the development of more accurate and robust classifiers for the prediction of moral sentiment.