Offer a Different Perspective: Modeling the Belief Alignment of Arguments in Multi-party Debates (2022)

Abstract: In contexts where debate and deliberation are the norm, the participants are regularly presented with new information that conflicts with their original beliefs. When required to update their beliefs (belief alignment), they may choose arguments that align with their worldview (confirmation bias). We test this and competing hypotheses in a constraint-based modeling approach to predict the winning arguments in multi-party interactions in the Reddit Change My View and Intelligence Squared debates datasets. We adopt a hierarchical generative Variational Autoencoder as our model and impose structural constraints that reflect competing hypotheses about the nature of argumentation. Our findings suggest that in most settings, predictive models that anticipate winning arguments to be further from the initial argument of the opinion holder are more likely to succeed.

## introduction on social media, individuals are often exposed to information that conflicts with their beliefs, which may result in them experiencing cognitive dissonance @xcite . in the context of multi-party online debates, the commenter (c) tries to change the opinion holder's (o) point of view. we may observe a confirmation bias, when exposed to information that conflicts with their beliefs, people may seek out and favor supporting arguments that are closest to their own beliefs @xcite , leading to polarized online spaces @xcite . however, there is also evidence to support that novel information could work in favor of changing the o's view. based on evidence from three different online experiments, @xcite reported that "when people are exposed to (new) information, they update their views in the expected or 'correct' direction, on average." * suzanna sia & kokil jaidka co-lead this work. which paradigm better describes the norms of online and offline debates? work on modeling persuasion in online forums has focused on identifying debate winners @xcite @xcite and winning negotiation games @xcite . most of this work focuses on engineering or learning features to predict argument success. previous work has suggested that to be persuasive, the commenter (c) should exert influence @xcite , and perform effective social interaction @xcite and language interplay @xcite . many of these studies suggest that interplay with the original opinion holder (o) would support a competing argument. however, it is not clear whether in this interaction, c should try to reflect o's views and build arguments from there, or try to directly bring in 'new' counter-arguments. in this study, we explore whether implementing the interplay of o and c via modeling constraints in variational autoencoders can identify winning counter-arguments. our contributions are: our study addresses this puzzle through a computational linguistic analysis of reddit change my view discussions. we also test the generalizability of our framework to a second dataset (iq2 debates). the state of the art in the space of argument modeling typically focuses on data representation. for instance, @xcite propose the construction of "discourse networks" using news coverage of political debates to analyze how discursive elements influence policy making. @xcite utilize semantic language representations and dynamics between debate transcripts, topics and interlocutors. our purpose is to reformulate the problem in terms of the adversarial nature of arguments posed during the debate. on the other hand, computational methods to study the idea of confirmation bias and the proclivity to change one's opinion have been explored with a focus on author and text attributes @xcite @xcite . extensive study has also been done with the primary dataset that we use for experimentation -the change my view (cmv) dataset @xcite . @xcite investigate people's reasoning behaviour in online forums and dayter and messerli (2021) discuss the degree of formality in persuasive speech. these approaches mainly focus on characterizing the linguistic properties of convincing arguments @xcite or the characteristics of an influential or susceptible opinion holder @xcite . instead, our study suggests that the latent spatial projection representing winning arguments are best interpreted in relation to the their distance from the original opinion holder's comment, as well as from other losing or irrelevant arguments. our work focuses on the inter-relation of the arguments to understand persuasion in debates. work that follows similar intuition as ours has proposed an attention mechanism that identifies the more malleable sections of the opinion holder's speech and interaction encodings which establish relationships between the opinion holder and commenter's speech @xcite . instead, our hierarchical variational autoencoder (vae) model characterizes the commenter's relative alignment (or misalignment) with the opinion holder. @xcite propose a weaklysupervised hierarchical latent variable model that utilises broader persuasiveness of textual requests along with limited sentence annotations to predict persuasion strategies on a sentence level. their work discretizes these persuasion strategies along specific axes @xcite . our work, on the other hand, focuses on the persuader and their alignment with that of the person being persuaded. to summarize, although there are many different ways in which prior work has conceptualized persuasive arguments in online contexts, the contribution of this work is in proposing a new way to model conversation stance in adversarial paradigms, by representing any argument relative to each other. we benchmark our approach against transformers @xcite and models inter-speech dynamics @xcite reported in previous work. ## problem formulation our problem formulation relies on the notion of beliefs, recently operationalized as the latent space projections of a semantic representation into lower dimensions for the purpose of representing and conveying certain ideas and concepts @xcite . therefore, in our primary task, we pose the following assumptions on the data generation process, i.e., how cmv debates occur: (a) all arguments in a "thread of conversation" are co-dependent for context, and constitute a "belief"; (b) irrelevant beliefs differ in topic but not intent, so constitute arguments on other topics; (c) the winning argument or belief is independent of others and is decided by the original poster o, and (d) all threads are independent of each other. we discuss these assumptions again and their possible relaxation in our generalizability test. let the latent states of opinion holders (o) and commenters (c) be depicted by the latent spaces occupied by the text of their arguments. their beliefs are thus the hidden vectors in a hierarchical variational autoencoder (vae). that is, we denote the opinion holder's and commenter's text instances x o and x c , and the beliefs modelled with hidden vectors (figure 1 ) as z o and z c . the goal is to predict whether the o has been persuaded by c. in the "change my view" (cmv) subreddit, we indicate successful counter-arguments with a ∆ and non-successful counter-arguments with ∅. we abuse notation for convenience but without loss of generality, and refer to both the opinion holder, as well as the opinion holder's opinions as o. the notation's position as a superscript indicates whether we are referring to this as a text instance (x) or with hidden vectors (z). we frame competing hypotheses to test whether opinion holders penalize or reward commenters who conflict with their worldview. although irrelevant counter-arguments are never directly fed into the ∆ predictor, the presence of irrelevant counter-arguments serves as an additional distance constraint for the relevant counter-arguments so that they are not arbitrarily far away from the original post. ## modeling approach we adopt a hierarchical generative model to model constraints on the latent spaces depicting the beliefs of o and c, denoted z o , and z c respectively. the hierarchical generative framework @xcite was applied to argument modeling by @xcite , but we extended the work by @xcite to consider multi-party interactions. this is because, within each main thread, there can be multiple commenters c trying to obtain a ∆ from the o. in subsection 4.1, we describe the implementation of hypothesis-specific modeling constraints, which is the main contribution of this paper. in subsection 4.2, we explain the general form of posterior inference over the latent states z given the observed content x, p(z|x), which is common to all hypotheses. ## results benchmarking against the state of the art. the benchmarking results in ## conclusion a hierarchical vae model was applied to model belief alignment in multi-party interactive arguments. the findings suggest that in debating contexts, participants may be open to changing their views when they encounter novel information. an evident limitation is that our findings are correlational and may only apply when participants are open to changing their point of view. yet, they offer the potential to model dialogue in new contexts invoking persuasion, influence, and cognitive dissonance, such as exposure to misinformation.