Considering a conversation thread, stance classification aims to identify the opinion (e.g. agree or disagree) of replies towards a given target. The target of the stance is expected to be an essential component in this task, being one of the main factors that make it different from sentiment analysis. However, a recent study shows that a target-oblivious model outperforms target-aware models, suggesting that targets are not useful when predicting stance. This paper re-examines this phenomenon for rumour stance classification (RSC) on social media, where a target is a rumour story implied by the source tweet in the conversation. We propose adversarial attacks in the test data, aiming to assess the models robustness and evaluate the role of the data in the models performance. Results show that state-of-the-art models, including approaches that use the entire conversation thread, overly relying on superficial signals. Our hypothesis is that the naturally high occurrence of target-independent direct replies in RSC (e.g. "this is fake" or just "fake") results in the impressive performance of target-oblivious models, highlighting the risk of target instances being treated as noise during training.

翻译：考虑到一个对话线程，立场分类旨在识别回复对给定目标的观点（如同意或不同意）。立场的目标被认为是该任务中的一个关键组成部分，使其与情感分析有所区别的主要因素之一。然而，一项近期研究表明，忽略目标的模型性能优于考虑目标的模型，暗示目标在预测立场时并无用处。本文重新审视了社交媒体上谣言立场分类（RSC）中的这一现象，其中目标是由对话中源推文隐含的谣言故事。我们在测试数据中提出对抗性攻击，旨在评估模型的鲁棒性及数据在模型性能中的作用。结果表明，包括使用完整对话线程的方法在内的最先进模型，过度依赖于表面信号。我们的假设是，RSC中自然高频率存在的独立于目标的直接回复（例如“这是假的”或仅仅“假的”），导致了忽略目标模型取得显著性能，这凸显了在训练过程中目标实例被当作噪声处理的风险。