Mis- and disinformation online have become a major societal problem as major sources of online harms of different kinds. One common form of mis- and disinformation is out-of-context (OOC) information, where different pieces of information are falsely associated, e.g., a real image combined with a false textual caption or a misleading textual description. Although some past studies have attempted to defend against OOC mis- and disinformation through external evidence, they tend to disregard the role of different pieces of evidence with different stances. Motivated by the intuition that the stance of evidence represents a bias towards different detection results, we propose a stance extraction network (SEN) that can extract the stances of different pieces of multi-modal evidence in a unified framework. Moreover, we introduce a support-refutation score calculated based on the co-occurrence relations of named entities into the textual SEN. Extensive experiments on a public large-scale dataset demonstrated that our proposed method outperformed the state-of-the-art baselines, with the best model achieving a performance gain of 3.2% in accuracy.

翻译：互联网上的虚假与误导信息已成为引发各类网络危害的主要社会问题。其中一种常见形式是脱离语境（OOC）信息，即不同信息片段被错误关联，例如真实图像搭配虚假文字说明或具有误导性的文字描述。尽管过往研究尝试通过外部证据抵御OOC虚假与误导信息，但往往忽略了不同立场的证据所发挥的作用。基于"证据立场对检测结果存在偏向性"这一直觉，我们提出立场提取网络（SEN），该网络可在统一框架下提取多模态证据的立场。此外，我们引入基于命名实体共现关系计算的支持-反驳分数，并将其融入文本SEN中。在公开大规模数据集上的大量实验表明，所提方法优于当前最优基线模型，最佳模型在准确率上实现了3.2%的性能提升。