Intelligent Framework to Detection of Fake News Using Deep Learning Approach

In the vast realm of social media, the insidious spread of fake news has become an unsettling reality, blurring the lines between fact and fiction. Misleading narratives and fabricated stories on social platforms can sway public opinion, erode trust, and hinder informed decision-making. The swift escalation of false information through likes, shares, and retweets underscores the pressing need for media literacy and critical thinking in the digital age. Fake news not only undermines the credibility of reputable sources but also poses significant challenges to the integrity of democratic discourse and societal cohesion. To combat the menace of fake news, a collective effort is required from users, platforms, and authorities to verify, share responsibly, and uphold the truth in the online landscape. Fake news is misinformation that is spread with the intent of personal benefit. It may be political or financial gain. Fake news has even influenced public opinion to a large extent in recent years. With the negative impact created by false information on gullible people's lives, we are prompted to develop systems to takle the issue of fake news's proliferation. Through this paper, we have done a comprehensive study of existing tools and techniques and further employed classification as multi-class and binary with help of many algorithms of Machine Learning like Random Forest, Support Vector Machine, Naive Bayes, Decision Tree, Logistic Regression(LR) and models of Deep Learning as Convolutional Neural Network, Long Short Term Memory. By utilizing the Convolutional Neural Network Algorithm, we achieved the maximum accuracy in our experiments.