This study examines how fake news affects social media users across a range of age groups and how machine learning (ML) and artificial intelligence (AI) can help reduce the spread of false information. The paper evaluates various machine learning models for their efficacy in identifying and categorizing fake news and examines current trends in the spread of fake news, including deepfake technology. The study assesses four models using a Kaggle dataset: Random Forest, Support Vector Machine (SVM), Neural Networks, and Logistic Regression. The results show that SVM and neural networks perform better than other models, with accuracies of 93.29% and 93.69%, respectively. The study also emphasises how people in the elder age group diminished capacity for critical analysis of news content makes them more susceptible to disinformation. Natural language processing (NLP) and deep learning approaches have the potential to improve the accuracy of false news detection. Biases in AI and ML models and difficulties in identifying information generated by AI continue to be major problems in spite of the developments. The study recommends that datasets be expanded to encompass a wider range of languages and that detection algorithms be continuously improved to keep up with the latest advancements in disinformation tactics. In order to combat fake news and promote an informed and resilient society, this study emphasizes the value of cooperative efforts between AI researchers, social media platforms, and governments.

翻译：本研究探讨了虚假新闻如何影响不同年龄段的社交媒体用户，以及机器学习（ML）和人工智能（AI）如何帮助减少虚假信息的传播。本文评估了多种机器学习模型在识别和分类虚假新闻方面的效能，并考察了虚假新闻传播的当前趋势，包括深度伪造技术。研究使用Kaggle数据集评估了四种模型：随机森林、支持向量机（SVM）、神经网络和逻辑回归。结果表明，SVM和神经网络的性能优于其他模型，准确率分别为93.29%和93.69%。研究还强调，老年群体对新闻内容批判性分析能力的减弱使其更容易受到虚假信息的影响。自然语言处理（NLP）和深度学习方法有望提高虚假新闻检测的准确性。尽管技术有所发展，但AI和ML模型中的偏见以及识别AI生成信息的困难仍然是主要问题。研究建议扩展数据集以涵盖更广泛的语言，并持续改进检测算法以跟上虚假信息策略的最新发展。为打击虚假新闻并促进一个知情且具有韧性的社会，本研究强调了AI研究人员、社交媒体平台和政府之间合作努力的重要性。