  As social media platforms are evolving from text-based forums into
multi-modal environments, the nature of misinformation in social media is also
transforming accordingly. Taking advantage of the fact that visual modalities
such as images and videos are more favorable and attractive to the users and
textual contents are sometimes skimmed carelessly, misinformation spreaders
have recently targeted contextual connections between the modalities e.g., text
and image. Hence many researchers have developed automatic techniques for
detecting possible cross-modal discordance in web-based content. We analyze,
categorize and identify existing approaches in addition to challenges and
shortcomings they face in order to unearth new research opportunities in the
field of multi-modal misinformation detection.
