Identifying Morality Frames in Political Tweets using Relational Learning (2021)

Abstract: Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions. The Moral Foundation Theory identifies five moral foundations, each associated with a positive and negative polarity. However, moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities. In this paper, we introduce morality frames, a representation framework for organizing moral attitudes directed at different entities, and come up with a novel and high-quality annotated dataset of tweets written by US politicians. Then, we propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly. We do qualitative and quantitative evaluations, showing that moral sentiment towards entities differs highly across political ideologies.

## introduction morality is a set of principles to distinguish between right and wrong. shared moral values form the social and cultural norms that unite social groups @xcite . moral foundations theory (mft) @xcite provides a theoretical framework for analyzing different expressions of moral values. the theory suggests that there are at least five basic moral values, emerging from evolutionary, social, and cultural origins. these are referred to as moral foundations (mfs), each with a positive and a negative polarity, and include care/harm, fairness/cheating, loyalty/betrayal, authority/-subversion, and purity/degradation ( while both tweets use the care/harm mf, in the top tweet (conservative) the aca is described as causing harm, while in the bottom (liberal), the aca is described as providing the needed care. our main contribution in this paper is to introduce morality frames, a representation framework for organizing moral attitudes directed at different targets, by decomposing the moral foundations into structured frames, each associated with a predicate (a specific mf) and typed roles. for example, the morality frame for care/harm is associated with three typed roles: entity providing care, entity needing the care, and entity causing harm. we focus on analyzing political tweets, each describing an eliciting situation that evokes the moral sentiment, and map the text to a mf, and the entities appearing in it to typed roles. given tweets by different ideological groups discussing the same real-world situation, morality frames can provide the means to explain and compare the attitudes of the two groups. we build on the mf dataset by @xcite consisting of political tweets, and annotate each tweet with mf roles for its entities. identifying moral roles from text in our setting requires inferences based on political knowledge, mapping between the author's perspectives and the judgements appearing in the text. for example, donald trump is likely to elicit a negative moral judgement from most liberals and a positive one from most conservatives, regardless of the specific moral foundation that is evoked. from a technical perspective, our goal is to model these kind of dependencies in a probabilistic framework, connecting mf and roles assignments, entity-specific sentiment polarity and repeating patterns within ideological groups (while our focus is u.s. politics, these settings could be easily adapted to capture patterns based on other criteria). we formulate these dependencies as a structured learning task and compare two relational learning frameworks, psl @xcite and @xcite . our experiments demonstrate that modeling these dependencies, capturing political and social knowledge, result in improved performance. in addition, we conduct a thorough ablation study and error analysis to explain their impact on performance. finally, we demonstrate how entity-based mf analysis can help capture perspective differences based on ideological lines. we apply our model to tweets by members of congress on the issue of abortion and the 2021 storming of the us capitol. our analysis shows that while conservative and liberal tweets target the same entities, their attitudes are often conflicting. ## related work usage of sociological theories like the moral foundation theory (mft) @xcite and framing @xcite @xcite in natural language processing tasks has gained significant interest. the moral foundation theory (mft) has been widely used to study the effect of moral values on human behavioral patterns, such as charitable donations @xcite , violent protests @xcite and social homophily @xcite . framing is a strategy used to bias the discussion on an issue towards a specific stance by emphasizing certain aspects that prime the reader to support the stance. framing is used to study the political bias and polarization in social and news media @xcite @xcite @xcite @xcite . moral foundation theory (mft) is frequently used to analyze political framing and agenda setting. for example, @xcite analyzed framing in partisan news sources using mft, @xcite studied the difference in moral sentiment usage between liberals and conservatives. @xcite found that moral/emotional political messages are diffused at higher rates on social media. previous works have also contributed to the detection of moral sentiments. @xcite showed that policy frames @xcite help in moral foundation prediction, hoover et al. (2020) proposed a dataset of 35k tweets annotated for moral foundations, lin et al. (2018) used background knowledge for moral sentiment prediction, xie et al. ( 2019) proposed a text based framework to account for moral sentiment change, and @xcite used pretrained distributed representations of words to extend the moral foundations dictionary @xcite for detecting moral rhetoric. while existing works study mft at the issue and sentence level, @xcite showed that there is a correlation between entity mention and the sentence-level moral foundation in the tweets by the u.s. politicians. we extend this work by studying mft directly at the entity level. hence, our work is broadly related to the works on entity-centric affect analysis @xcite @xcite . combining neural networks and structured inference was explored for traditional nlp tasks such as dependency parsing @xcite @xcite , named entity recognition @xcite and sequence labeling systems @xcite . recently, these efforts have expanded to discourse-level tasks such as argumentation mining @xcite , event/temporal relation extraction @xcite and discourse representation parsing @xcite . following this trend, pacheco and goldwasser (2021) introduced drail, a general declarative framework for deep structured prediction, designed specifically for nlp tasks. in this paper, we use drail to model moral foundations and morally-targeted entities in tweets, and find an improvement over other non-neural probabilistic graphical modeling frameworks @xcite . ## model we propose a relational learning model for identifying morality frames in text. we begin by defining our relational structure (sec. 4.1) and proceed to describe its implementation using relational learning tools (sec. 4.2). ## experimental evaluation the goal of our relational learning framework is to identify morality frames in tweets by modeling them jointly, and derive interpretable relations between them and other contextualizing information. in this section, we compare the performance of our model with multiple baselines, and present a detailed error analysis. then, we collect tweets on one topic (abortion) and one event (2021 us capitol storming) written by us congress members and analyze the discussion. 1 we identify the morality frames in these tweets using our best model. ## analyzing political discussions in this section, we first characterize the political discussion on abortion using the predicted morality frames. then, we analyze how an event impacts the moral sentiment towards entities by looking at the usage of mf roles before and after the 2021 us capitol storming for the different parties. ## summary in this paper, we present the first study on moral foundations theory at the entity level, by assigning moral roles to entities, and present a novel dataset of political tweets that is annotated for this purpose. we propose a relational model that predicts moral foundations and the moral roles of entities jointly, and show the effectiveness of modeling dependencies and contextualizing information for this task. finally, we analyze political discussions in the us using these predictions, and show the usefulness of our proposed schema. in the future, we intend to study how morality frames and our relational framework can be applied in other settings, where contextualizing information is not observed.