Cyberspace News Prediction of Text and Image with Report Generation

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.