Fake News and Hate Speech Detection Using Deep Learning

Abstract

Due to the potential for such content to hurt both people and society as a whole, the issue of identifying fake news and hate speech on internet platforms has grown in importance in recent years. This problem has shown great potential for resolution through deep learning, a specific area of machine learning. In this research work, we examine recent studies on the application of deep learning methods for the identification of fake news and hate speech. We begin by outlining the difficulties in identifying such information, such as the difficulty in defining fake news and hate speech, the sheer volume of online text, and the requirement to take context and other contextual elements into account. The use of deep learning for detection is then covered in detail. In order to enhance the performance of models, we also investigate the use of transfer learning and data augmentation strategies. An active study field in NLP and machine learning is the detection of fake news and hate speech using deep learning algorithms. The detection of fake news and hate speech has showed promise when using deep learning algorithms like CNNs, RNNs, and transformers. Using methods like sentiment analysis, topic modeling, and text classification, one can identify false news by examining the news article's wording, citations, and tone. Lack of big, annotated datasets is a barrier in detecting fake news and hate speech, particularly in languages other than English. For the development of precise deep learning models, high-quality annotated datasets are crucial.