The task of rumour verification in social media concerns assessing the veracity of a claim on the basis of conversation threads that result from it. While previous work has focused on predicting a veracity label, here we reformulate the task to generate model-centric free-text explanations of a rumour's veracity. The approach is model agnostic in that it generalises to any model. Here we propose a novel GNN-based rumour verification model. We follow a zero-shot approach by first applying post-hoc explainability methods to score the most important posts within a thread and then we use these posts to generate informative explanations using opinion-guided summarisation. To evaluate the informativeness of the explanatory summaries, we exploit the few-shot learning capabilities of a large language model (LLM). Our experiments show that LLMs can have similar agreement to humans in evaluating summaries. Importantly, we show explanatory abstractive summaries are more informative and better reflect the predicted rumour veracity than just using the highest ranking posts in the thread.

翻译：社交媒体中的谣言验证任务旨在基于对话线程评估声明的真实性。以往研究聚焦于预测真实性标签，而本文将该任务重新定义为生成以模型为中心的谣言真实性自由文本解释。该方法具有模型无关性，可推广至任意模型。本文提出一种基于图神经网络的新型谣言验证模型。我们采用零样本方法：首先应用事后可解释性方法对线程中最关键的帖子进行评分，随后利用这些帖子通过观点引导摘要生成信息性解释。为评估解释性摘要的信息量，我们利用大语言模型的少样本学习能力。实验表明，大语言模型在评估摘要时能与人类达成相近的共识。重要的是，我们证明相比仅使用线程中排名最高的帖子，生成式抽象摘要更具信息性，且能更准确地反映预测的谣言真实性。