Social cohesion, democratic processes, and public trust are all seriously threatened by the spread of hate speech and fake news in the digital age. This study looks at how difficulties may be solved by automated detection and mitigation using Natural Language Processing (NLP) tools such Long Short-Term Memory (LSTM) networks and BERT. The usefulness of machine learning models, which can classify fake news with up to 98% accuracy, is examined by the researchers, who also draw attention to the difficulties in detecting hate speech because of variations in laws and culture. Along with contemporary legal frameworks like the EU’s Digital Services Act and the divergent approaches of the U.S. First Amendment, ethical issues such as algorithmic bias, privacy concerns, and transparency are critically assessed. In order to guarantee that NLP technologies are in line with social values, the study emphasizes the need for multidisciplinary cooperation, strong policy implementation, and culturally sensitive AI systems. In order to promote fair digital discourse, future paths will prioritize explainable AI, sophisticated cross-lingual models, and ethical frameworks.https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5181145Share this:FacebookXLike this:Like Loading... Post navigation Kenya develops National Guidelines to tackle digital disinformation and hate speech (UNESCO) Assessing the Hatefulness of Social Media Posts: A Continuous Measure of Hate Using Generative AI (SSRN)