Conversations among online users sometimes derail, i.e., break down into
personal attacks. Such derailment has a negative impact on the healthy growth
of cyberspace communities. The ability to predict whether ongoing conversations
are likely to derail could provide valuable real-time insight to interlocutors
and moderators. Prior approaches predict conversation derailment
retrospectively without the ability to forestall the derailment proactively.
Some works attempt to make dynamic prediction as the conversation develops, but
fail to incorporate multisource information, such as conversation structure and
distance to derailment.
We propose a hierarchical transformer-based framework that combines
utterance-level and conversation-level information to capture fine-grained
contextual semantics. We propose a domain-adaptive pretraining objective to
integrate conversational structure information and a multitask learning scheme
to leverage the distance from each utterance to derailment. An evaluation of
our framework on two conversation derailment datasets yields improvement over
F1 score for the prediction of derailment. These results demonstrate the
effectiveness of incorporating multisource information.

提出一种基于分层 Transformer 的框架，结合话语级别和对话级别信息捕捉精细的上下文语义，同时考虑话语结构信息和距离变形的多任务学习方案，以实现对网络对话中马车出轨的预测。

对话建模预测出轨

Conversation Modeling to Predict Derailment

The tremendous growth of social media users interacting in online
conversations has also led to significant growth in hate speech. Most of the
prior works focus on detecting explicit hate speech, which is overt and
leverages hateful phrases, with very little work focusing on detecting hate
speech that is implicit or denotes hatred through indirect or coded language.
In this paper, we present CoSyn, a user- and conversational-context synergized
network for detecting implicit hate speech in online conversation trees. CoSyn
first models the user's personal historical and social context using a novel
hyperbolic Fourier attention mechanism and hyperbolic graph convolution
network. Next, we jointly model the user's personal context and the
conversational context using a novel context interaction mechanism in the
hyperbolic space that clearly captures the interplay between the two and makes
independent assessments on the amounts of information to be retrieved from both
contexts. CoSyn performs all operations in the hyperbolic space to account for
the scale-free dynamics of social media. We demonstrate the effectiveness of
CoSyn both qualitatively and quantitatively on an open-source hate speech
dataset with Twitter conversations and show that CoSyn outperforms all our
baselines in detecting implicit hate speech with absolute improvements in the
range of 8.15% - 19.50%.

本文提出了一种基于超伸缩空间的用户与对话上下文配合的机制 CoSyn，用于在线讨论中检测隐含仇恨言论，该方法在开放源 Twitter 数据集上的实验表明：CoSyn 在检测隐含仇恨言论方面表现出比其它方法更好的性能。

CoSyn: 基于上下文协同的双曲网络用于在线对话中隐含仇恨言论的检测

CoSyn: Detecting Implicit Hate Speech in Online Conversations Using a Context Synergized Hyperbolic Network

A rapidly increasing amount of human conversation occurs online. But
divisiveness and conflict can fester in text-based interactions on social media
platforms, in messaging apps, and on other digital forums. Such toxicity
increases polarization and, importantly, corrodes the capacity of diverse
societies to develop efficient solutions to complex social problems that impact
everyone. Scholars and civil society groups promote interventions that can make
interpersonal conversations less divisive or more productive in offline
settings, but scaling these efforts to the amount of discourse that occurs
online is extremely challenging. We present results of a large-scale experiment
that demonstrates how online conversations about divisive topics can be
improved with artificial intelligence tools. Specifically, we employ a large
language model to make real-time, evidence-based recommendations intended to
improve participants' perception of feeling understood in conversations. We
find that these interventions improve the reported quality of the conversation,
reduce political divisiveness, and improve the tone, without systematically
changing the content of the conversation or moving people's policy attitudes.
These findings have important implications for future research on social media,
political deliberation, and the growing community of scholars interested in the
place of artificial intelligence within computational social science.

本研究利用大规模实验展示了如何用人工智能工具改善有争议话题的在线对话，从而提高参与者认为自己在会话中被理解的感受和对话的质量，减少政治分歧和恶意，而不改变对话内容或人们的政策态度。