TRACKING AND TRACING OF FAKE NEWS USING URL
Dr P Pritto Paul, Deepthi V, Geeta Lakshmi P and Pooja Thanushree K
Associate Professor, Velammal Engineering College, India, E-mail: prittopaul.p@velammal.edu.in
UG Scholars, Department of Computer Science Engineering, India, E-mail: deepthivanivijay@gmail.com
UG Scholars, Department of Computer Science Engineering, India, E-mail: geetalakshmipittip@gmail.com
UG Scholars, Department of Computer Science Engineering, India, E-mail: poojathanushreek@gmail.com
Abstract:- With the increasing quality of social media, individuals have modified the means they access news. The aborning increase in pretend News because of social media’s intensive usage could be a massive downside in today's world. Any background as well as policy, crime, health, or Pandemics like Covid-19 may be coupled to pretend news. To avoid its outsourcing at the person level, communities of social media actively work to resolve the problems to avoid the danger display by information on-line. Some pretend news square measure almost like the 64000 ones that it's tough for human to spot them. Therefore, machine controlled pretend news detection tools became an important demand. During this system we tend to appraise the fakeness and realism of stories victimization, using five machine learning models (ML) and two deep learning models (DL) with novel stacking model. They'll observe advanced patterns in matter information. This method works for wide numerous real time links; it ranges from varied on-line social media like Facebook, twitter, Instagram, google sites etc. The links are URLs. To pretend blogs, pretend websites that deceive the users in a way or the opposite, this method conjointly dynamically collects datasets from user. They conjointly enable to report the pretend news manufacturing links to crime. Thus, victimization of the novel stacking approach performance of system has enlarged.
Key words: Uniform Resource Locator (URL), Machine Learning (ML), Deep Learning (DL)
