  Fake news and misinformation are a matter of concern for people around the
globe. Users of the internet and social media sites encounter content with
false information much frequently. Fake news detection is one of the most
analyzed and prominent areas of research. These detection techniques apply
popular machine learning and deep learning algorithms. Previous work in this
domain covers fake news detection vastly among text circulating online.
Platforms that have extensively been observed and analyzed include news
websites and Twitter. Facebook, Reddit, WhatsApp, YouTube, and other social
applications are gradually gaining attention in this emerging field.
Researchers are analyzing online data based on multiple modalities composed of
text, image, video, speech, and other contributing factors. The combination of
various modalities has resulted in efficient fake news detection. At present,
there is an abundance of surveys consolidating textual fake news detection
algorithms. This review primarily deals with multi-modal fake news detection
techniques that include images, videos, and their combinations with text. We
provide a comprehensive literature survey of eighty articles presenting
state-of-the-art detection techniques, thereby identifying research gaps and
building a pathway for researchers to further advance this domain.
