Detection of Fake Profiles and News on Social Media Using Machine Learning Algorithms

Technology is advancing rapidly every day, becoming an integral part of our lives and helping people connect with each other through social media and online news outlets. But as everything has its own advantages and disadvantages, it has too, and this paves a way for individuals to make fake news and accounts that are posing a major threat to society. This paper tries to incorporate a strategy utilizing Machine learning algorithms. Random Forest, Support Vector Machines and Neural Network are some of the algorithms which are used to distinguish fake profiles on social media and for detection of fake news online Naïve Bayes' and LSTM are utilized. Hence, detection of fake profiles and fake news online is possible using the above-mentioned frameworks. The outcome illustrates the ability to distinguish between fake and real profiles using extracted features by applying trained machine learning models. Naive Bayes, k-nearest neighbor, and Poisson calculation independently give precision of 87%, 88.3.5%, and 91.2% individually. The proposed approach shows that the algorithm neural networks system has an accuracy of 94.3%. The proposed work uses the machine learning approach for detection of tampered profiles on social media platforms.