Terrorism And Fake News Detection

Abstract

Fake news dissemination is a critical issue in today’s fast-changing network environment. The issues of online fake news have attained an increasing eminence in the diffusion of shaping news stories online. This paper deals with the categorical cyber terrorism threats on social media and preventive approach to minimize their issues. Misleading or unreliable information in form of videos, posts, articles, URLs are extensively disseminated through popular social media platforms such as Facebook, Twitter, etc. As a result, editors and journalists are in need of new tools that can help them to pace up the verification process for the content that has been originated from social media. existing classification models for fake news detection have not completely stopped the spread because of their inability to accurately classify news, thus leading to a high false alarm rate. This study proposed a model that can accurately identify and classify deceptive news articles content infused on social media by malicious users. The news content, social-context features and the respective classification of reported news was extracted from the PHEME dataset using entropy-based feature selection. The selected features were normalized using Min-Max Normalization techniques. The model was simulated and its performance was evaluated by benchmarking with an existing model using detection accuracy, sensitivity, and precision as metrics. The result of the evaluation showed a higher 17.25% detection accuracy, 15.78% sensitivity, but lesser 0.2% precision than the existing model, Thus, the proposed model detects more fake news instances accurately based on news content and social content perspectives. This indicates that the proposed classification model has a better detection rate, reduces the false alarm rate of news instances and thus detects fake news more accurately.