Abstract

The widespread propagation of misinformation, especially during pandemics, are often followed by severe consequences, that include public distrust in healthcare systems, increased anxiety, and disturbances in critical treatments. Thus, a robust system is needed to detect misinformation as well as to prevent its rapid spread to maintain stability and avoid disorganization in the society. This study proposes a novel hybrid artificially intelligent model which combines a bidirectional encoder representations from transformers (BERT)-based neural network and term frequency–inverse document frequency (TF–IDF) to detect and reduce misinformation effectively. The framework integrates clear steps to guarantee reproducibility, including fusion of TF–IDF and contextual BERT embeddings through concatenation, feature normalization using min–max scaling and classification using fully connected layers. The model achieves an accuracy of 96–98% on benchmark datasets and consistently outperforms existing methodologies. The efficacy of this method in identifying and tracking misinformation trends, especially during health crises has been demonstrated by the experimental results, whereas the integration of explainable artificial intelligence (XAI) helps to improve transparency.

Keywords

Misinformation Detection, BERT, TF-IDF, Explainable artificial intelligence (XAI), Pandemic information analysis.

Cite this article

Goswami S, Bhattacharya S, Poray J. Context-aware misinformation detection using BERT-based neural network with TF-IDF integration. International Journal of Advanced Computer Research. 2026;16(75):23-37. DOI : 10.19101/IJACR.2025.1570016

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