The unregulated proliferation of counterfeit news creation and dissemination that has been
seen in recent years poses a constant threat to democracy. Fake news articles have the power to
persuade individuals, leaving them perplexed. This scientometric study examined 569 documents
from the Scopus database between 2012 and mid-2022 to look for general research trends, publication
and citation structures, authorship and collaboration patterns, bibliographic coupling, and productivity patterns in order to identify fake news using deep learning. For this study, Biblioshiny and
VOSviewer were used. The findings of this study clearly demonstrate a trend toward an increase in
publications since 2016, and this dissemination of fake news is still an issue from a global perspective.
Thematic analysis of papers reveals that research topics related to social media for surveillance and
monitoring of public attitudes and perceptions, as well as fake news, are crucial but underdeveloped,
while studies on deep fake detection, digital contents, digital forensics, and computer vision constitute
niche areas. Furthermore, the results show that China and the USA have the strongest international
collaboration, despite India writing more articles. This paper also examines the current state of the art
in deep learning techniques for fake news detection, with the goal of providing a potential roadmap
for researchers interested in undertaking research in this fiel
