Cyberspace News Prediction of Text and Image with Report Generation

Abstract

The cyberspace news consumption is
increasing day by day all over the world. The main
reason for cyber space news consumption is due to its
rapid spread of information and its easy access which
lead people to consume news rapidly without the
knowledge of whether the news is false or true. Thus, it
leads to the wide spread of false news which leads to the
negative impacts on society. Therefore false news
prediction on cyberspace is attracting a tremendous
attention. The issue of fake-news prediction on
cyberspace is both challenging and relevant as
spreading of fake news occurs in various streams like
text, audio, video, images etc. This model works on
processing the text and images together by providing an
interactive Application Interface (API), i.e. text by
applying the model Logistic regression classifier and
image by applying self-consistency algorithm. The
natural language tool kit (NLTK) model is used for
these implementation through python. Once the news is
predicted fake, a report is redirected to the authorized
website (cybercrime department) to take the immediate
necessary actions required to stop these news from
spreading.