Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation

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 outof-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-ofthe-art baselines, with the best model achieving a performance gain of 3.2% in accuracy. * Corresponding co-authors 1 In the literature the terms "misinformation" and "disinformation" often have inconsistent definitions. In our work, we adopt the more established definitions by the United Nations ( https://www.undp.org/eurasia/dis/ misinformation ): misinformation refers to information that is false but not created with the intention of causing harm and disinformation to information that is false and deliberately created to cause harm. Our work can be applied to both mis-and disinformation, so we will mostly use the term "mis-/disinformation".