Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory (2021)

Abstract: The Moral Foundation Theory suggests five moral foundations that can capture the view of a user on a particular issue. It is widely used to identify sentence-level sentiment. In this paper, we study the Moral Foundation Theory in tweets by US politicians on two politically divisive issues - Gun Control and Immigration. We define the nuanced stance of politicians on these two topics by the grades given by related organizations to the politicians. First, we identify moral foundations in tweets from a huge corpus using deep relational learning. Then, qualitative and quantitative evaluations using the corpus show that there is a strong correlation between the moral foundation usage and the politicians’ nuanced stance on a particular topic. We also found substantial differences in moral foundation usage by different political parties when they address different entities. All of these results indicate the need for more intense research in this area.

## introduction over the last decade political discourse has shifted from traditional news outlet to social media. these platforms give politicians the means to interact with their supporters and explain their political perspectives and policy decisions. while formulating policies and passing legislation are complex processes which require reasoning over the pros and cons of different alternatives, gathering support for these policies often relies on appealing to peoples' "gut feeling" and invoking an emotional response @xcite . moral foundation theory (mft) provides a theoretical framework for analyzing the use of moral sentiment in text. the theory @xcite suggests that there are a small number of moral values, emerging from evolutionary, cultural and social reasons, which humans support. these are referred to as the moral foundations (mf) and include care/harm, fairness/cheating, loyalty/betrayal, authority/subversion, and purity/degradation. this theory was used to explain differences between political ideologies, as each side places more or less value on different moral foundations @xcite . liberals tend to emphasize the fairness moral foundation, for example, consider the following tweet discussing the 2021 mass shooting event in colorado, focusing on how the race of the shooter changes the coverage of the event. liberal gun control tweet. fairness @ilhanmn the shooter's race or ethnicity seems front and center when they aren't white. otherwise, it's just a mentally ill young man having a bad day. on the other hand, conservatives tend to place more value on loyalty. the following tweet discusses the same event, emphasizing solidarity with the families of victims and the broader community. conservative gun control tweet. loyalty @repkenbuck my prayers are with the families of the victims of today's tragedy in boulder. i join the entire community of boulder in grieving the senseless loss of life. i am grateful for the officers who responded to the scene within minutes. you are true heroes. in this paper, we study the relationship between moral foundation usage by politicians on social media and the stances they take on two policy issues, gun control and immigration. we use the dataset provided by @xcite for training a model for automatically identifying moral foundations in tweets. we then apply the model to a collection of 74k and 87k congressional tweets discussing the two issues -gun control and immigration, respectively. our analysis goes beyond binary liberal-conservative ideological labels @xcite . we use a scale of 5 letter grades assigned to politicians by relevant policy watchdog groups, based on their votes on legislation pertaining to the specific policy issue. we analyze the tweets associated with the members of each group. furthermore, we hypothesize that even when different groups use similar moral foundation, they aim to invoke different feelings in the readers. to capture these differences, we analyze the targets of the moral tweets by different groups. our analysis captures several interesting trends. first, the proportion of non-moral tweets on both issues decreases as grades move from a (most conservative) to f (most liberal), while for the topic of gun control (immigration), the proportion of harm (loyalty) tweets increases. second, even when using the same moral foundation, their targets differ. for example, when discussing gun control, using the loyalty moral foundation, liberal mostly mention march life, gabby gifford, while conservatives mention gun owner, texas. ## related works the moral foundation theory (mft) @xcite has been proven to be useful in explaining social behaviour of humans @xcite @xcite @xcite . recent works have shown that political discourse can also be explained using mft @xcite @xcite . existing works explain the political discourse mostly at issue and sentence level @xcite @xcite and at left-right polar domains of politics. several works have looked at analyzing political ideologies, beyond the left and right divide, using text @xcite , and specifically using twitter data @xcite @xcite . to the best of our knowledge, this is the first work that studies whether mft can be used to explain nuanced political standpoints of the us politicians, breaking the left/right political spectrum to nuanced standpoints. we also study the correlation between entity mentions and moral foundation usage by different groups, which helps pave the way to analyze partisan sentiment towards entities using mft. in that sense, our work is broadly related to entity-centric affective analysis @xcite @xcite . we use a deep structured prediction approach (pacheco and @xcite to identify moral foundations in tweets by being motivated from the works that combine structured prediction with deep neural networks in nlp tasks @xcite @xcite . ## dataset in this section, we describe the data collection process to analyze the us politicians' stances and sentiment towards entities on the topics -immigration and gun control. first, we discuss existing datasets. then, we create a topic specific lexicon from existing resource to identify topics in new data. finally, we collect a large tweet corpus on the two topics using a lexicon matching approach. ## identification of moral foundation in tweets to identify moral foundations in the collected dataset, we rely on a supervised approach using a deep relational learning framework. in this section, we first describe the model we use for the supervised classification. then, we describe our training procedure and analyze the performance of our model on a held out set. finally, we describe the procedure to infer moral foundations in the collected dataset using our model. ## analysis of politicians' nuanced stances in this section, we analyze the nuanced stances of us politicians on the topics gun control and immigration, using moral foundation theory. first, we define nuanced political stances. then we study the correlation between the moral foundation usage and nuanced political stances. ## analysis of partisan sentiment towards entities in this section, we study the partisan sentiment towards entities by examining the usage of moral foundations while discussing the entities. first, we extract entities from the tweets, then we analyze the usage of moral foundations in the context of those entities by the two opposite parties. ## future work in this section, we discuss some potential research directions that our analyses may lead to and their application in understanding political discourse. our experiments in section 4 show that joint modeling of multiple aspects of the dataset (e.g. text, issue, and political affiliation) and the dependency among multiple decisions (e.g. temporal dependency), helps in classification. incorporating other information such as linguistic cues, behavioural aspects, and so on, has the potential to improve the prediction furthermore. in general, incorporating information from multiple sources (e.g. social, textual) and modeling dependencies among decisions is an interesting future work that can help in the identification of the underlying intent of the text. so, this framework may be extended to similar tasks, such as political framing analysis, misinformation analysis, propaganda detection, and so on. in section 5, we found out that moral foundation usage can be useful in explaining the nuanced political stances of politicians beyond the left/right discreet categories. we observed that usage of some moral foundations strongly correlates with the nuanced stances of the politicians. while the stances of the extreme left (grade f) and extreme right (grade a) politicians are easy to explain, what are the stances of the politicians in the middle (grades b to d), is yet to be investigated qualitatively. this line of research would help in understanding the stance of the politicians at individual levels and has real-life implications. for example, understanding politicians' individual stances would help determine their future vote on legislative decisions and to identify the aisle-crossing politicians. in section 6, we found out clear cases where sentiment towards entities can be explained by grounding the moral foundation theory at the entity level. this is an interesting direction where we can seek answers to several research questions, such as, (r1) what are the dimensions in a moral foundation category along which the sentiment towards the entities can be explained?; (r2) can sentiment towards entities, inspired from moral foundations, explain political discourse?; (r3) do the sentiment towards entities change over time and in response to real-life events? we believe our analyses will help advance the research in this direction. ## summary in this paper, we study how moral foundation theory (mft) can explain nuanced political stances of us politicians and take the first step towards partisan sentiment analysis targeting different entities using mft. we collect a dataset of 161k tweets authored by us politicians, on two politically divisive issues, gun control and immigration. we use a deep relational learning approach to predict the moral foundations in the tweets, that models tweet text, topic, author's ideology, and captures temporal dependencies based on publication time. finally, we analyze the politicians' nuanced standpoints and partisan sentiment towards entities using mft. our analyses show that both phenomena can be explained well using mft, which we hope will help motivate further research in this area.