Promoting Constructive Deliberation: Reframing for Receptiveness (2024)

Abstract: To promote constructive discussion of controversial topics online, we propose automatic reframing of disagreeing responses to signal receptiveness to a preceding comment. Drawing on research from psychology, communications, and linguistics, we identify six strategies for reframing. We automatically reframe replies to comments according to each strategy, using a Reddit dataset. Through human-centered experiments, we find that the replies generated with our framework are perceived to be significantly more receptive than the original replies and a generic receptiveness baseline. We illustrate how transforming receptiveness, a particular social science construct, into a computational framework, can make LLM generations more aligned with human perceptions. We analyze and discuss the implications of our results, and highlight how a tool based on our framework might be used for more teachable and creative content moderation.

## introduction constructive deliberation and debate amongst disagreeing views promotes sharing ideas and collective intelligence for innovation, decision-making, and governance @xcite . such exchanges also promote empathy and tolerance, rather than isolation and polarization @xcite . however, online discussion today is often hostile and confrontational, where exposure to opposing views in this setting can instead provoke even greater polarization @xcite . current methods for promoting pro-social discussion and debate online are limited. for example, community guidelines may principally focus on preventing harms rather than promoting pro-social norms. in addition, moderator controls are often fairly coarse: simply blocking users or posts is scalable but rigid, while crafting open-ended messages * denotes equal author contributions to guide behavior is extremely flexible but can be difficult to scale @xcite . nlp research for assisting scalable content moderation has also largely focused on anti-social tasks such as removing toxic speech @xcite , reducing offensive language with style-transfer @xcite , and removing content that violates community rules @xcite . while some studies have begun to pursue content moderation through the lens of promoting pro-social content @xcite @xcite , promoting constructive deliberation has received relatively little attention. this is a difficult task, involving nuanced and subtle linguistic cues that underlie productive discussions. research from communications, psychology, and linguistics suggests how we might promote open-mindedness to opposing views. in particular, yeomans et al. (2020)'s conceptualization of conversational receptiveness focuses on convey-ing openness while still disagreeing. others have studied how to measure receptiveness towards disagreeing perspectives @xcite ). however, there has been limited success in translating such theory into practice. in this work, we seek to bridge nlp research on textual reframing @xcite @xcite with social science theories on receptiveness. our aim is to promote pro-social deliberation online; particularly to foster productive discussion for contentious issues. we develop a computational framework to signal receptiveness while preserving the original meaning of reframed content, studying the capabilities of large language models (llms) for this task. we illustrate the value of translating social science theory to a computational framework by showing how model generations created with our framework better align with human notions of receptiveness, allowing for more useful content moderation tools. models that are more receptive-aligned might allow for content moderation tools that are valuable to users and moderators despite being automated. for moderators, a tool built on our receptiveness framework could allow for more scalable and creative moderation. for users, such a tool might provide learning opportunities, suggesting possible revisions if their post is flagged as unreceptive. users could learn how to be more receptive from suggestions, whether or not they accept them. here, we operationalize receptiveness through a framework of six lower-level strategies from social science theory to make messages more receptive to opposing viewpoints. we ground this conceptual work in a concrete study of reddit posts, illustrated in figure 1 . we compile a new corpus of automatically reframed reddit replies and assess (via automatic measures and human evaluation) the validity of the generated replies. we compare reframes generated by our framework to original replies and baselines. we find they are significantly more aligned with what humans find receptive, eliciting relatively low negative emotional reactions, high curiosity to engage with opposing views, and reduced belief that the user is trying to shut down discussion. we also explore the interaction between toxicity and receptiveness reframing, finding that more toxic content benefits more from reframing. we end with broader discussion of receptiveness for content moderation and mitigating polarization. we create and share a corpus of receptive-reframed replies, baseline reframes, and human annotations of receptiveness (16.6k reframes and over 9.2k total annotations) foot_0 . ## approach our broad goal is to foster pro-social online discussions in which participants constructively engage with others having opposing viewpoints. to this end, we pursue automated techniques to reframe online discussions, i.e., rephrasing messages in a manner that would preserve a message's original meaning while modifying its tone to convey greater openness to opposing viewpoints. in section 3.1, we introduce and motivate six specific methods we apply for conveying receptiveness curated from social science literature that we transform into reframing strategies. these are also illustrated in figure 1 . following this, we describe our specific task formulation and data used (section 3.2). finally, we describe our method for automating this reframing (section 3.3). ## validation experiments we next present analyses conducted to validate our reframe generation methods. we assess whether our reframes satisfy three key properties: 1) demonstrate the six distinct strategies for receptiveness; 2) preserve the meaning of original replies being reframed; and 3) are contextually relevant to the original comment. ## discussion differences in receptiveness between factors as discussed in section 5, our reframes have a smaller effect on reducing bias (f3) than on any of the other factors. this presents promising opportunities for future work on receptiveness reframing aimed at generating reframes that reduce outgroup bias. for example, this could include explicit instruction to reduce bias in addition to our prompts, or attempting to signal impartiality or humility (alsheddi, 2020; fisher, 2000) as ways to implicitly reduce bias. however, these methods might result in a tradeoff between reducing bias and meaning preservation. see limitations for more discussion. combining multiple strategies while we only generate reframes using one strategy at a time, future work could explore combining strategies. we do find that certain receptiveness reframes contain strategies that were not explicitly prompted for; this was especially prominent with hedges, which were commonly added to generations using other strategies. understanding how different combinations of strategies might affect perceived receptiveness would allow us to refine the current framework. future work could also automatically optimize for which strategies to use in different contexts. receptiveness for polarization increasing affective polarization has contributed to partisan gridlock (hetherington and rudolph, 2020), and more recently, limited compliance with covid-19 mitigation policies @xcite . a major intervention known to reduce polarization is actually fairly straightforward: to engage in conversations with the other side (levendusky and stecula, 2021). we believe that our computational framework for reframing, if integrated into conversation spaces, could improve the quality of conversations between opposing partisans, potentially reducing polarization @xcite . ## conclusion this work illustrates how transforming the social science construct of receptiveness into a computational framework can make llm generations more aligned with human perceptions. we found that our reframed replies are perceived to be better aligned with human notions of receptiveness than a reply generated with a generic receptiveness prompt. we also analyzed how our reframes are perceived in terms of the different receptiveness factors, and how some (emotion, curiosity, openness) are perceived to have improved more than others (bias). finally, we demonstrated that our reframes appear to be more effective when the original replies are more toxic, and discuss applications of receptiveness-signaling for content moderation and reducing polarization.