Abstract:
The modem digital era produces a large volume of data on an hourly basis around the
world including Pakistan. This data contains a significant amount of false, misleading,
and fake information and claims. One of the major reasons for the unstable political
situation in Pakistan is the spreading of fake claims. These fake claims result in a quick
fire around the public causing unfavourable events. Although these fake claims may
get exposed as false or invalid claims later, the damage had been done till then. The
reason is the manual research for the validation of these claims which takes lot of time
to find out the truth. Automated claim detectors and validators can fill this gap. It will
result in preventing serious damages by providing the reality of claims in a matter of
few minutes. By using advanced natural language processing and machine learning
techniques which involve Recurrent Neural Network (RNN), LSTM, Bi-LSTM, CNN
and GRU, we have prepared a model trained on past Pakistani political and influential
claims and their reality. It is capable of detecting claims in sentences and validating
them. To make this model easily accessible to everyone we have made a ReactJS-
based modem web application with the Django backend along with the ClaimBuster,
arXiv and OpenAI API. It integrates the ClaimCracker model with the interactive user
interface of the ClaimCracker web application. This dynamic web application provides
a way for the end user to detect and validate claims with just a single button click. The
model is capable of providing 93% accuracy for detecting and validating claims based
on Pakistani politics and current affairs.
