Unveiling the Power of Argument Arrangement in Online Persuasive Discussions (2023)

Abstract: Previous research on argumentation in online discussions has largely focused on examining individual comments and neglected the interactive nature of discussions. In line with previous work, we represent individual comments as sequences of semantic argumentative unit types. However, because it is intuitively necessary for dialogical argumentation to address the opposing viewpoints, we extend this model by clustering type sequences into different argument arrangement patterns and representing discussions as sequences of these patterns. These sequences of patterns are a symbolic representation of argumentation strategies that capture the overall structure of discussions. Using this novel approach, we conduct an in-depth analysis of the strategies in 34,393 discussions from the online discussion forum Change My View and show that our discussion model is effective for persuasiveness prediction, outperforming LLM-based classifiers on the same data. Our results provide valuable insights into argumentation dynamics in online discussions and, through the presented prediction procedure, are of practical importance for writing assistance and persuasive text generation systems.

## introduction convincing others can be quite challenging, even when equipped with a comprehensive set of arguments. the questions then arise: what kind of arguments are the most convincing and which should be presented first? should one begin with facts or personal experiences? the different answers to these and related questions are referred to as argumentation strategies @xcite . several studies have empirically examined the arrangement of argumentative discourse unit (adu) types, such as facts or testimonies, in monological texts (e.g., @xcite ), or in individual comments within dialogues (e.g., @xcite ; @xcite ). these studies have shown that the cmv: today is the best time period in human history to be alive for the vast majority of people. with all the negativity, we really need to focus on the data and the positive @xcite . there is almost not a single important data point that you can say is trending up over time @xcite . wars, famine, human rights, healthcare, inclusion, infant mortality @xcite . the list goes on and on and on. the only big change had been access to negative information @xcite , but the actual texture of the world has gotten way better over time @xcite . what am i missing? i'm looking for someone to point out the flaw in my logic. the only thing i can see is our human nature is not supposed to be this connected to a constant flow of information @xcite , but that's easy to turn off. please help change my view. currently in the developed world we are living through an unprecedented mental health epidemic with skyrocketing suicides @xcite . cost of living is outstripping earnings for the majority of workers and houses are becoming so expensive[v] that i, someone who makes well above the national mean, can no longer live in the city i grew up in @xcite . my generation is the first that is predicted not to live as long as the previous generation on average @xcite and all of this is taking place to a backdrop of the existential threat that is climate change @xcite . the best time period passed us by recently[v] but we no longer live in it @xcite . the mental health crises i think is very real so for that. … the best time was easily early 2010s up to 2017 @xcite , since then we've seen the first time civil rights have been rolled back @xcite . plenty of societies have collapsed before[f], there's no reason ours can't @xcite . so no, today is absolutely not the best time to be alive @xcite . we're in a backwards shift, hopefully we can recover but we can hardly assume we will without actively addressing the issues that have led to our backwards decline @xcite . what are you talking about?![r] because atrocities …. arrangement of adu types in a text can serve as a model of the argumentation that underlies the text, for example for predicting its persuasiveness. in dialogues, such as the two discussion branches shown in figure 1 , it seems intuitive that there is no single best argumentation strategy for either side. instead, the strategy needs to be dynamically adapted in response to the ongoing conversation. for instance, if the opponent concludes their ar-gument with a fact, countering them with another fact before suggesting a policy might be more convincing. notably, this dialogic nature has been mostly overlooked in the computational analysis of argumentative discussions so far. in this paper, we examine the nuances of ongoing argumentation in dialogues with the goal of understanding the particular elements that contribute to the success of particular debaters. through a detailed analysis of adu sequences, we seek to reveal the underlying patterns and strategies utilized by skilled debaters. these strategies are essential for advancing both theoretical understanding and practical application. theoretically, they particularly improve our understanding of persuasive dialogues, enabling the refinement of skills among novice debaters and thereby enhancing the overall quality of discussions. in practical terms, these strategies serve as valuable guidance for the automated generation of persuasive texts that resonate effectively with various audiences. moreover, a set of core strategies can potentially form the backbone of advanced writing tools, providing substantial support to writers as they structure their arguments. to reveal these strategies, we introduce a new model for argument arrangement. this model includes identifying particular types of adus within a given argumentative discourse, analyzing the sequences of these adus, and clustering these sequences to reveal the patterns and strategies. we test our model using a large-scale dataset of persuasive discussions gathered from change my view. 1 we sampled two types of discussion branches, dialogue and polylogue, and identified 16 clusters of similar adu type patterns, each representing a different strategy used in the discussions. the sequence of clusters for each discussion (one cluster assignment per comment) then serves as a model of the discussion, which we evaluate against the task of determining the persuasiveness of the commenter. 2 for this task of persuasiveness detection, our model outperforms several strong baselines. overall, this paper introduces a new model for identifying argument arrangement strategies through clusters of similar adu type patterns (section 3). we develop a large-scale dataset comprising 34,393 discussion branches and completely tag it for adu types using a new approach that outperforms the previous state-of-the-art by 0.18 in terms of f 1 score (section 4). moreover, we use our model to identify clusters representing the arrangement strategies used in these discussions, and show the utility of cluster sequences for argumentation analysis through the example task of predicting persuasiveness (section 5). 3 ## related work argumentative discussions play a major role in computational argumentation analysis, for example when detecting counterarguments @xcite or whether two arguments are on the same side @xcite . a special emphasis is placed on the change my view platform due to its extensive user base, strict moderation, and user-provided persuasiveness rating. several prior works examined individual comments, where a wide range of linguistic, stylistic, and argumentative features have been employed to predict their persuasiveness (e.g., @xcite ; @xcite ; @xcite ; @xcite ). for analyses beyond individual comments, @xcite employ features of both comments and the corresponding opening post, but do not look at discussions that are longer than one comment to the opening post. @xcite consider whole discussions and employ several linguistic features, but none tied to arrangement. our paper makes a significant contribution to the task of predicting persuasiveness, going beyond exploring individual comments and delving into the arrangement of arguments within discussions. @xcite model unit types for premises (ethos, logos, and pathos) and claims (interpretation, evaluation, agreement, or disagreement), revealing that the relative position of types can indicate a comment's persuasiveness. @xcite , on the other hand do not distinguish between premises and claims. they use the following types: testimony, fact, value, policy, or rhetorical statements. we employ the types of @xcite for their simplicity, having shown impact on persuasiveness, and the easy to understand semantics behind the types (cf. section 4.3), making them well-suited for modeling argument arrangement. ## preparing a dataset of argument arrangements in online discussions to evaluate the effectiveness of our argument arrangement model, we applied it to real-world online discussions data, specifically focusing on the task of predicting persuasiveness. we collected a dataset comprising 34,393 discussion branches, extracted from 1,246 threads sourced from the change my view online discussion platform via the public reddit api. these discussions adhere to a structured format enforced by the community, with persuasive discussions being identified by the presence of a ∆ character, in accordance with community guidelines, which serves as our ground truth (section 4.1). for our analysis, we categorize discussions into two scenarios, dialogue and polylogue (section 4.2), and automatically identify argumentative discourse units (adus) and their respective semantic types (section 4.3). subsequently, we offer initial insights into the use of adu types through a statistical analysis of their positional distribution within a discussion branch (section 4.4). ## analysis of experiments and results in this section, we present the results of modeling argument arrangement of argumentative discourse unit (adu) types introduced in section 3.3 and propose several approaches for predicting the persuasiveness of discussion branches. furthermore, we conduct an experimental validation of our findings by incorporating cluster features in the task of persuasiveness prediction. ## conclusion this paper expanded our understanding of online persuasive dialogues by uncovering that debaters follow certain arrangement strategies. we introduced a new model for arrangement strategies that is based on patterns of argumentative discourse unit types. clusters of such patterns correspond to different strategies, with sequences of these clusters (one element per comment of a discussion branch) representing a whole discussion. this model was operationalized using a large-scale dataset comprising 34,393 discussion branches. in a comparative evaluation of ten approaches, we demonstrate the remarkable utility of these arrangement strategies in predicting persuasiveness -both if used as sole feature and in addition to others -, emphasizing their essential role in unraveling the dynamics of persuasive online discussions. still, there's ample room for refining and expanding this research further. one aspect worth exploring is the development of more fine-grained categories for argumentative discourse units, such as incorporating the human value categories proposed by @xcite . besides, the identified arrangement strategies have applications beyond persuasiveness prediction. exploring their potential in tasks such as writing assistance and text generation could broaden the scope of argumentative discourse analysis into new horizons.