  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.
