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pakistani_dataset_consolidated.xlsx (6506989, 2022-08-18)

# Fake-news-detection-on-Pakistani-news-using-machine-learning-and-deep-learning Fake news causes a huge impact on the readers mind, therefore it has become a major concern. Identifying fake news or differentiating between fake and authentic news is quite challenging. The trend of fake news in Pakistan has grown a lot in the last decade. This research aims to develop the first comprehensive fake news detection dataset for Pakistani news by using multiple fact-checked news APIs. This research also evaluates the developed dataset by using multiple state-of-the-art artificial intelligence techniques. Five machine learning techniques namely Naive Bayes, KNN, Logistic Regression, SVM, and Decision Trees are used. While two deep learning techniques CNN and LSTM are used with GloVe and BERT embeddings. The performance of all the applied models and embeddings is compared based on precision, F1-score, accuracy, and recall. The results show that LSTM initialized with GloVe embeddings has performed best. The research also analyzes the misclassified samples by comparing such samples with human judgments. Link for the paper: https://www.sciencedirect.com/science/article/abs/pii/S0957417422016244 Please cite: Azka Kishwar, Adeel Zafar, Fake news detection on Pakistani news using machine learning and deep learning, Expert Systems with Applications, 2022, 118558, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.118558. (https://www.sciencedirect.com/science/article/pii/S0957417422016244)

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