Detecting Minority Arguments for Mutual Understanding: A Moderation Tool for the Online Climate Change Debate (2022)

Abstract: Moderating user comments and promoting healthy understanding is a challenging task, especially in the context of polarized topics such as climate change. We propose a moderation tool to assist moderators in promoting mutual understanding in regard to this topic. The approach is twofold. First, we train classifiers to label incoming posts for the arguments they entail, with a specific focus on minority arguments. We apply active learning to further supplement the training data with rare arguments. Second, we dive deeper into singular arguments and extract the lexical patterns that distinguish each argument from the others. Our findings indicate that climate change arguments form clearly separable clusters in the embedding space. These classes are characterized by their own unique lexical patterns that provide a quick insight in an argument’s key concepts. Additionally, supplementing our training data was necessary for our classifiers to be able to adequately recognize rare arguments. We argue that this detailed rundown of each argument provides insight into where others are coming from. These computational approaches can be part of the toolkit for content moderators and researchers struggling with polarized topics.

## introduction even though a consensus has existed within the scientific community on the topic of human-caused climate change for some time, the online debate remains very polarized. online comment spaces are typically overwhelmed with a large quantity of contributions. this information flood hinders the promotion of mutual understanding and inclusivity in debate spaces. additionally, climate change presents a splintered debate with niche opinions and many viewpoints. the recognition of these niche arguments are vital to support the moderator in adhering to the heterogeneous discussion that climate change presents. this setting presents opportunities for mutual understanding by improving issue awareness and the quality of deliberation. in this paper, we construct a twofold approach to support mutual understanding in the online climate change discussion. first, we aim to classify posts for the argument they present. second, we dive deeper into singular arguments to create an overview of the lexical patterns in each argumentspecific sub-corpus. we conclude the paper by discussing the limitations of modeling nuanced argumentation by a computational method and link our approach to fields struggling with content moderation and polarized debates. ## discussion we presented an approach to automatically label online comments for the argument it entails, combined with a deeper dive into each argument in the discussion. in the upcoming paragraphs, we go through some methodological considerations and discuss our approach through the lens of content moderation. furthermore, we reflect on the usefulness of our approach for other fields that struggle with mutual understanding and opinion polarization. translating detailed and nuanced concepts of argumentation into a computational labelling task requires generalization. @xcite make the useful distinction between scepticism, uncertainty, and ambivalence. in our annotation scheme, we did not make this specific contrast. whereas clear-cut scepticism can be rare, as is shown in our data, uncertainty about the anthropogenic causes of climate change might be much more widespread @xcite . the dichotomy between uncertainty and scepticism may be an important aspect for mutual understanding and working towards the comprehension and acceptance of human-caused climate change. unstable or uncertain beliefs can change through contact with scientific cues and information @xcite . in this paper, uncertainty is included within the argument classes, even though the label refers to scepticism. future research could include this distinction in the methodology to encompass the nuance of polarized debates into the computational approach. researchers in the field of content moderation and digital journalism struggle with the concept of mutual understanding, as well as with the implementation of computational technologies @xcite . the growing quantity of contributions threatens real-time curation efforts by human moderators. automatic applications like the one we have presented in this paper are an avenue for assisting human moderators in curating the online comment space @xcite . while the moderators manage ongoing, interactive processes that are highly dependent on context, computational systems can assist this operation, for example in the form of argument classification and summaries. furthermore, research fields that specifically deal with polarized topics struggle with safeguarding civil discussion and mutual understanding. the climate change debate certainly falls within this category. additionally, online debates on the topic of vaccination lack mutual understanding as well @xcite . this discussion often lacks heterogeneous discussion due to so-called 'echo chamber' effects @xcite . computational moderation tools, like the one presented in this paper, are an asset for those invested in promoting mutual understanding in these polarized discussions. this approach can be expanded beyond the topic of climate change into other polarized topics. clearly defined arguments are needed. an example of such a discussion is vaccination, in which clear pro and con sides can be detected @xcite . domain-specific research is a requirement to create annotation schemes that adequately entail all minority arguments. ## conclusion in this paper, we created a twofold approach to develop a moderation tool aimed at the climate change debate on online platforms. first, we trained classifiers that label comments for the argument they present. certain minority arguments, like trend scepticism and accusations of bad science, were very rare. an active learning approach was constructed with the goal of collecting more minority arguments to supplement into our train-ing dataset. our best model, after two waves of uncertain posts, achieved a macro f1-score of 0.78. second, we dove deeper into singular arguments by extracting the lexical patterns that characterize each class. the arguments formed clusters in the embedding space, indicating that each reasoning may be characterized by specific vocabularies. these patterns serve as a swift and understandable view into each argument. additionally, we formulated methodological considerations regarding the nuance in the annotation scheme and linked our approach to research fields that struggle with moderating online content while safeguarding understanding among participants. the computational approach presented in this paper serves an assisting role to the human moderator, who in turn can deal with the contextual factors.