M.S. AAI Capstone Chronicles 2024

Detecting Fake News Using Natural Language Processing classifiers. Deep learning methods such as Long Short-Term Memory (LSTM) networks, featuring single-layer, stacked, and bi-directional configurations, are employed to capture temporal dependencies in textual data, alongside advanced models like DistilBERT, to discern semantic intricacies within textual data. The first phase of model training involved employing various supervised learning algorithms, including logistic regression, decision trees, gradient boosting, and random forest classifiers. The data was split into training and testing sets using a 75-25 ratio. The accuracy scores for the random forest were the best among all classification models. Cross-validation techniques such as stratified k-fold validation and hyperparameter tuning using grid search were utilized further to enhance model robustness and performance. In the second phase of training, tokenization and padding of text data were performed. Data was split into training and testing sets using an 80-20 ratio. Three different types of LSTM models were trained: basic LSTM, stacked LSTM, and bidirectional LSTM. Each LSTM model was compiled using the RMSprop optimizer and binary cross-entropy loss function. Confusion matrices and classification reports were generated to evaluate the performance of each model on the testing data, with the bidirectional LSTM model achieving the highest accuracy. In the third phase, we took advantage of Bi-LSTM’s capability to learn from both past and future sequences simultaneously and evaluated multilayer bidirectional LSTM models. The two-layer model emerged as the most favorable option due to its relatively simpler architecture, shorter training duration, and efficient parameter utilization. Despite being simpler, it achieved commendable accuracy and demonstrated efficient resource utilization compared to its counterparts where more layers were involved.

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