Catching Fake News with Machine Learning: A Practical System that Actually Explains Itself

There are too many misinformation on the internet. days, and old-school factchecking is no longer able to do the same. So, we built a Hybrid machine learning system combining experience algorithms. with profound intelligence to snare counterfeit news soon and with accuracy–no matter what language it’s in. Our setup looks beyond just the words; it disintegrates the style of writing, and the meaning, testing: On three large datasets, LIAR (12,836 verified). statements), ISOT (44,898 full articles), and FakeNewsNet (23,196). social media posts). we make use of an ensemble– think Naive Bayes, SVM, random forest, Bidirectional LSTMs, and a fine-tuned. And BERT model, all the same. Weighted just right, they hit Accuracy of telling real and fake 94.8 percent. For transparency, we introduced SHAP and LIME, and you may properly see why the model made its choice. And here is the punchline– not that only. English. There is still 87- on Spanish, French, and Hindi data. It doesn’t even need special tweaks to have 91 percent accuracy. Each article takes approximately 2.8 seconds to process making real-time moderation no longer wishful thinking. Bottom line: this framework supports scale, speed, languages, and explainability, which are precisely what. content moderation must have today. Index Terms-Misinformation detection, ensemble learning. natural language processing, explainable AI, content verification, SHAP, LIME.