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

Understand

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.