The news platform has moved from traditional newspapers to online communities in the technologically advanced area of Artificial Intelligence. Because Twitter and Facebook allow us to consume news much faster and with less restricted editing, false information continues to spread at an impressive rate and volume. Online Fake News Detection is a promising field in research and captivates the attention of researchers. The sprawl of huge chunks of misinformation in social network platforms is vulnerable to global risk. This article recommends using a Machine Learning optimization technique for automated news article classification on Facebook and Twitter. The emergence of the research is facilitated by the strategic implementation of Natural Language Processing for social forum fake news findings in order to distort news reports from non-recurrent outlets. The relent from the study is outstanding with text document frequency words, which act as extraction technique’s attribute, and the classifier is acted upon by Hybrid Support Vector Machine by achieving 91.23% accuracy.

该新闻平台已从传统报纸转向技术先进的人工智能领域的在线社区。因为 Twitter 和 Facebook 使我们能够更快地消费新闻，编辑限制更少，虚假信息继续以惊人的速度和数量传播。在线假新闻检测是一个很有前途的研究领域，吸引了研究人员的注意。社交网络平台中大量错误信息的蔓延很容易受到全球风险的影响。本文建议使用机器学习优化技术对 Facebook 和 Twitter 上的自动新闻文章进行分类。自然语言处理对社交论坛虚假新闻发现的战略实施促进了该研究的出现，以歪曲非经常性媒体的新闻报道。研究结果突出，文本文档频率词作为提取技术的属性，分类器采用混合支持向量机，准确率达到91.23%。