  Spreading of misleading information on social web platforms has fuelled huge
panic and confusion among the public regarding the Corona disease, the
detection of which is of paramount importance. To address this issue, in this
paper, we have developed an automated system that can collect and validate the
fact from multi web-platform to decide the credibility of the content. To
identify the credibility of the posted claim, probable instances/clues(titles)
of news information are first gathered from various web platforms. Later, the
crucial set of features is retrieved that further feeds into the ensemble-based
machine learning model to classify the news as misleading or real. The four
sets of features based on the content, linguistics/semantic cues, similarity,
and sentiments gathered from web-platforms and voting are applied to validate
the news. Finally, the combined voting decides the support given to a specific
claim. In addition to the validation part, a unique source platform is designed
for collecting data/facts from three web platforms (Twitter, Facebook, Google)
based on certain queries/words. This unique platform can also help researchers
build datasets and gather useful/efficient clues from various web platforms. It
has been observed that our proposed intelligent strategy gives promising
results and quite effective in predicting misleading information. The proposed
work provides practical implications for the policy makers and health
practitioners that could be useful in protecting the world from misleading
information proliferation during this pandemic.
