WH o W : A Cross-domain Approach for Analysing Conversation Moderation (2025)

Abstract: We propose WHoW, an evaluation framework for analyzing the facilitation strategies of moderators across different domains/scenarios by examining their motives (Why), dialogue acts (How) and target speaker (Who). Using this framework, we annotated 5,657 moderation sentences with human judges and 15,494 sentences with GPT-4o from two domains: TV debates and radio panel discussions. Comparative analysis demonstrates the framework’s cross-domain generalisability and reveals distinct moderation strategies: debate moderators emphasise coordination and facilitate interaction through questions and instructions, while panel discussion moderators prioritize information provision and actively participate in discussions. Our analytical framework works for different moderation scenarios, enhances our understanding of moderation behaviour through automatic large-scale analysis, and facilitates the development of moderator agents.

## introduction conversational moderation typically involves a moderator who upholds an impartial stance and interest, to facilitate and coordinate discussions among participants through conversation @xcite . moderation occurs in diverse human interactive settings, however, the role of the moderator varies from hosts of debates @xcite , judges in judicial processes @xcite , to therapists in group therapy sessions @xcite . while there are various definitions of moderation across different domains @xcite @xcite the concept is generally characterized as a form of discourse optimization mechanism with the essential objectives of: (1) mitigation: preventing 1 our code, dataset are available at https://github.com/ mrknight21/conversation_moderation_analysis . and as a result, many students have borrowed have a lot of money. and especially in a downtime of economic growth when economic growth is so mediocre. ## related work conversation moderation is a complex task that requires consideration of multiple dimensions when making intervention decisions. this task takes place in multi-party settings @xcite , where a moderator's decisions regarding interventions and turn assignment @xcite @xcite must account for the conversation context, group dynamics, and the balance of participation. depending on the scenario, moderators fulfill various functional roles, such as inviting for contribution, providing background information, facilitating topic transitions, and posing questions to guide discussions and maintain their quality @xcite @xcite . furthermore, moderators often operate under hybrid motives, which include facilitating quality arguments @xcite , maintaining social engagement @xcite , and managing external factors like time constraints @xcite . ultimately, moderation is a strategic task, requiring the application of specific strategies to encourage constructive contributions and participant engagement while minimizing destructive conflicts @xcite @xcite . the effect and influence of moderation have been studied across various domains using different analytical measures. in online mental health support forums, the presence of a moderator has been shown to improve user engagement, openness, linguistic coordination, and trust-building compared to non-moderated groups @xcite . in the educational domain, moderators have been found to enhance collaboration patterns and increase online participation rates in group learning settings @xcite . case studies and interviews have also been conducted to analyze the role and function of moderators in community building @xcite , focus group discussions @xcite , online public issue discussions and debates @xcite , and mediating contentious stakeholders @xcite . despite the existence of some annotation protocols and datasets, resources for conversational moderation remain notably limited. many studies have been conducted on small sample sizes @xcite and often do not make their datasets publicly available @xcite . additionally, the research often relies on methodologies such as interviews or case studies, which are not reusable for further analysis or automation @xcite . as of the time this study's manual annotation was conducted, the only anno-tated dataset currently available consists of just 300 comments @xcite 2 . furthermore, some studies treat moderation as a reactive intervention to participant comments and structure the data as comment-intervention pairs @xcite , thereby overlooking broader session-level objectives such as balancing participation and the overall role of the moderator. moreover, while several annotation protocols exist, they tend to be overly specific to their application domains. for instance, the role of "resolving site use issues" is only pertinent to e-rule-making scenarios @xcite and don't generalise to other domains. ## the whow conversational moderation analytic framework we design an analytical framework that: ( 1 ) is grounded in the existing literature @xcite @xcite ; ( 2 ) captures the multifaceted nature of conversational moderation; and (3) generalizes across different domains. our framework ( ## analysis we now analyze which dialogue acts are used to facilitate discussion and encourage participation. in appendix section d, we compare gpt-4o labels against human label distributions on the development and test set, showing that they are overall consistent, with the exception of the "instruction" and "supplement" acts, with only minor variations in magnitude. specifically, we examine how speaker rotation is facilitated and how the three motives are addressed across the two domains in the dataset. ## conclusion we present whow, an analytical framework that characterizes conversational moderation across domains. whow breaks down the complexity of moderation decision-making into three key components: why the moderator intervenes (motives), how they intervene (dialogue acts), and to whom they direct the intervention (target speakers). using this framework, we annotated moderation utterances in two distinct scenarios: intelligent squared debate corpus (debate) and roundtable radio panel discussion (panel). we showed that gpt-4o can effectively automate the labelling process. in total, our dataset has 5,657 human-annotated and 15,494 gpt-4o annotated moderation sentences, which is an order of magnitude larger than existing datasets @xcite . our analysis demonstrates the framework's effectiveness in differentiating intervention strategies and styles across the two scenarios. in debate, moderators are primarily coordination-motivated, serving functional roles as interviewers and instructors, while occasionally facilitating interaction between non-moderator speakers. in contrast, panel moderators are more information-oriented, acting as both contributors and interviewers, as they often participate in the discussion topics. while they seek information from the speakers and balance turn-taking, they promote less direct interaction between non-moderator speakers. our framework can serve as an exploratory tool or foundational skeleton for domain-specific adaptation and expansion in moderation analysis or moderator agent development. using raw transcripts, users can initially categorize the moderator's speech into the twelve categories across the motive and dialogue act dimensions, then refine these labels based on the specific domain context. for example, in a mental health support setting, "social interpretation" could be expanded into more specific categories like "emotion interpretation". although the current dataset may not be large enough for fine-tuning supervised models, it serves as a valuable resource for in-context few-shot learning. this means it provides practical examples that help develop models capable of predicting or recommending intuitive moderation interventions based on our framework. future studies should encompass a broader range of moderation scenarios, such as group counseling (kissil, 2016) and second language group conversations @xcite . additionally, the proposed analytic framework could be expanded to support the generation of conversational moderation strategies by sequentially predicting the three key components. another important direction is the development of evaluation metrics to assess the effects and potential biases of moderation interventions @xcite , enabling deeper insights into the impact and fairness of moderation practices. finally, a broader goal for future work could involve synthesizing the results into "moderator prototype strategies" -a schema with multiple axes capturing distinct moderation styles. as new scenarios are explored, these prototypes may evolve, offering a richer understanding of diverse moderation approaches.