Forecasting Conversation Derailments Through Generation (2025)

Abstract: Forecasting conversation derailment can be useful in real-world settings such as online content moderation, conflict resolution, and business negotiations. However, despite language models’ success at identifying offensive speech present in conversations, they struggle to forecast future conversation derailments. In contrast to prior work that predicts conversation outcomes solely based on the past conversation history, our approach samples multiple future conversation trajectories conditioned on existing conversation history using a fine-tuned LLM. It predicts the conversation outcome based on the consensus of these trajectories. We also experimented with leveraging socio-linguistic attributes, which reflect turn-level conversation dynamics, as guidance when generating future conversations. Our method of future conversation trajectories surpasses state-of-the-art results on English conversation derailment prediction benchmarks and demonstrates significant accuracy gains in ablation studies.

## introduction predicting future derailments from ongoing conversations has a wide range of real-world applications. for instance, in online moderation, the ability to forecast if a discussion thread might devolve into offensive exchanges allows moderators to intervene preemptively. similarly, in conflict resolution, political hearings, and business negotiations, a system that warns participants of impending conflicts could help mitigate disputes before they escalate. despite its utility, predicting future conversation derailments presents significant challenges. as shown in examples in figure 1 , unlike the detection of offensive conversational turns @xcite @xcite , which focuses on identifying harmful speech within a given conversation transcript, our task requires forecasting figure 1 : an example conversation from the bnc dataset, including background and the future turn. offensive speech is highlighted in red. our task requires forecasting whether the derailment would occur in the future based on the conversation so far. to address these challenges, we propose a novel approach that forecasts future derailments by generating potential continuations of the given conversation. we define conversation derailments as conversations that end with offensive speech or ad hominem attacks, with dataset-specific criteria and annotation procedures explained in section 4.1. our approach is based on the observation that while large language models (llms) may not be effective classifiers for forecasting conversation breakdowns directly, they excel at generating conversation continuations. thus, we adopt a generate-then-predict approach for forecasting conversation derailments. we first fine-tune an llm on modeling online conversations. we then sample multiple conversation continuations from this fine-tuned llm, conditioned on the existing conversation history. we feed each generated continuation along with the existing conversation history to a binary conversation derailment classifier and obtain multiple predictions. we determine the final conversation outcome by taking a majority vote of these individual predictions. by sampling multiple plausible continuations and aggregating the individual predictions with majority vote, we reduce the variability of stochastic llm outputs, leading to more robust and accurate forecasting of conversation derailments. additionally, inspired by prior work @xcite and the circumplex theory @xcite @xcite in psychology, we explore whether the use of social orientation labels can guide the generation of conversation continuations. social orientation labels (e.g., assertive, confrontational) are sociolinguistic attributes that reflect conversation dynamics and emotional states (figure 2 ), and have been shown to help computational models better capture the flow of conversation @xcite . we validate our approach on two datasets: the conversation gone awry wiki split (cga-wiki) dataset @xcite , derived from wikipedia editor discussions, and the before name calling (bnc) dataset @xcite , based on reddit's r/changemyview threads. our method demonstrates significant accuracy improvements compared to the previous state-of-the-art and fewshot gpt-4o. on the cga-wiki dataset, we achieve a 4-7% absolute accuracy improvement, while on the bnc dataset, our method yields an 18-20% improvement. extensive ablation studies further confirm the effectiveness of our proposed approach. in sum, our contributions are: our datasets and models are available through this github repository. ## problem statement and motivation our objective is to estimate the likelihood of future conversation derailment based on the benign conversation history up to a certain point. we define conversation derailments as offensive speech and ad hominem attacks, in line with previous work @xcite . formally, let @xmath0, . . . , c n } represent the conversation history consisting of n turns, where c i denotes the i-th turn in the dialogue. let y({c 1 , . . . , c i }) be a binary indicator function for conversation derailment over the turns {c 1 , . . . , c i }, where @xmath1. our goal is to estimate the likelihood of future derailments given the first k benign turns, expressed as: to illustrate the challenges of forecasting conversation derailment, we conducted an experiment comparing the performance of a language model in two settings: detecting offensive speech from a full conversation transcript and predicting future derailments based only on the conversation history prior to any offensive speech. we used bart-base @xcite as the underlying model, trained with a binary cross entropy loss to directly predict the conversation derailment. ## methodology figure 2 shows an overview of our approach. we employ a fine-tuned llm to generate multiple future continuations given the existing conversation history. we then train a dedicated classifier to assess the conversation outcome by analyzing both the existing and newly generated conversations. we determine the final outcome based on the majority vote of these individual predictions. we also study whether social orientation labels (see section 3.4) can be used to guide the text generation and conversation derailment classification. ## experiments we evaluate our conversation derailment prediction method on two datasets: conversation gone awry wiki split @xcite and the before name calling dataset @xcite . we describe the datasets and our experiment details below. ## results and analysis we show our experiment results on conversation gone awry wiki split (cga-wiki) and before name calling (bnc) in ## conclusion and future work we introduced a novel approach for forecasting conversation derailments by generating potential future conversation trajectories based on existing conversation history. this technique demonstrated strong performance on conversation derailment prediction benchmarks, such as the cga-wiki and bnc datasets. we validated the effectiveness of our method through comparisons with state-of-theart models and comprehensive ablation studies. we also assessed the contribution of social orientation labels in guiding derailment prediction. future research could extend our methodology to conversations beyond online discourse, such as in-person conversations and meetings.