Pioneer researches recognize evidences as crucial elements in fake news detection apart from patterns. Existing evidence-aware methods either require laborious pre-processing procedures to assure relevant and high-quality evidence data, or incorporate the entire spectrum of available evidences in all news cases, regardless of the quality and quantity of the retrieved data. In this paper, we propose an approach named \textbf{SEE} that retrieves useful information from web-searched annotation-free evidences with an early-termination mechanism. The proposed SEE is constructed by three main phases: \textbf{S}earching online materials using the news as a query and directly using their titles as evidences without any annotating or filtering procedure, sequentially \textbf{E}xamining the news alongside with each piece of evidence via attention mechanisms to produce new hidden states with retrieved information, and allowing \textbf{E}arly-termination within the examining loop by assessing whether there is adequate confidence for producing a correct prediction. We have conducted extensive experiments on datasets with unprocessed evidences, i.e., Weibo21, GossipCop, and pre-processed evidences, namely Snopes and PolitiFact. The experimental results demonstrate that the proposed method outperforms state-of-the-art approaches.

翻译：早期研究认识到，除了模式识别外，证据是虚假新闻检测中的关键要素。现有的证据感知方法要么需要繁琐的预处理流程来确保相关且高质量的证据数据，要么在所有新闻案例中不加区分地纳入全部可用证据，而忽略检索数据的质量与数量。本文提出一种名为 \textbf{SEE} 的方法，该方法通过提前终止机制从网络搜索的无标注证据中检索有用信息。所提出的 SEE 方法由三个主要阶段构建：以新闻内容作为查询 \textbf{S}earching 在线资料，并直接将其标题作为证据，无需任何标注或过滤流程；通过注意力机制依次 \textbf{E}xamining 新闻与每条证据，生成包含检索信息的新隐藏状态；以及在审查循环中允许 \textbf{E}arly-termination，通过评估是否具备足够置信度以产生正确预测。我们在包含未处理证据的数据集（即 Weibo21、GossipCop）和经过预处理证据的数据集（即 Snopes、PolitiFact）上进行了大量实验。实验结果表明，所提方法优于现有最先进方法。