NLP-Driven Misinformation Identification in Online News Content

The growth of online news platforms and social media has increased the speed at which information reaches the public. Alongsidelegitimate reporting, misleading, manipulated, and fabricated content can circulate widely before it is verified. Manual verification isvaluable but can be time-consuming when large volumes of articles must be reviewed. This paper proposes an NLP-driven approach foridentifying news content that resembles examples of misinformation in a labelled dataset. The proposed workflow includes text validation,preprocessing, feature extraction, supervised classification, result presentation, and human review. TF-IDF with Logistic Regression orMultinomial Naive Bayes is considered as a transparent baseline, while contextual embeddings may be explored for capturing semanticrelationships. The evaluation plan includes precision, recall, F1-score, accuracy, confusion-matrix analysis, and tests across differentsources or time periods. No experimental results are claimed because the proposed system has not been tested in a controlled experiment.The approach treats automated predictions as screening signals rather than definitive proof, since language patterns alone cannot establishfactual truth. The design emphasises dataset quality, transparent limitations, and responsible use.