Detecting Social Media Counterfeit Using Deep Learning Algorithm

The rapid dissemination of fake news via traditional and digital platforms has emerged as one of the most critical challenges of today's information-driven society. Fake news, which is also known as deliberately false or misleading information, has always been generated to manipulate public opinion and damage reputations or even exploit by sensationalism for profit. Twitter and Facebook have become some of the most prominent social media channels through which such types of false information flow. Studies show that false information travels much faster than true factual information. Thus, to overcome all these types of issues, this project aims to present a fake newsdetecting model based on advanced machine learning and Natural Language Processing (NLP) techniques that would automatically identify misleading content. The model also uses text mining algorithms to extract the meaningful pattern and linguistic features of news articles by NLP techniques such as tokenization, stemming, lemmatization, and named entity recognition (NER). The features thus extracted are then analyzed using classification algorithms such as Naive Bayes, Gradient Boosting, and Random Forest, in order to classify the authenticity of the content. Model evaluations are done using a performance metric such as accuracy and a confusion matrix with initial expectations favoring the higher accuracy of the Naive Bayes classifier. Word embedding and sentiment analysis are also added to represent the model's ability to distinguish between real and fake news. The proposed framework is intended to contribute to the implementation of a successful scalable solution for real-time detection of fake news.