In Natural Language Processing, detecting fake news is a crucial yet difficult subject (NLP). The fast emergence of social networking platforms has increased the dissemination of bogus news while simultaneously vastly increasing information accessible. As a result, the impact of fake news has grown, sometimes spilling over into the offline world and endangering public safety. Automatic fake news identification is a practical NLP problem helpful to all online content producers, given the vast volume of Web material, in order to decrease human time and effort in detecting and preventing the spread of fake news. In this study, we discuss the difficulties of detecting false news as well as associated issues. We go through the problem formulations, datasets, and NLP solutions which have been created for this job in detail, as well as their potentials and constraints. We present prospective research options based on our findings, including more fine-grained, detailed, fair, and practical detection models. We also discuss the distinctions between false news identification and other related problems, as well as the usefulness of natural language processing (NLP) solutions for fake news detection.



Software And Hardware