M.S. AAI Capstone Chronicles 2024

Detecting Fake News Using Natural Language Processing Figure 7: The pink and blue area represents the complex model’s decisions , which LIME is unaware of. The location of the bold red cross is the requested instance to be explained, the other crosses/circles are instances that use the model. These are then weighed based on how close it is to the requested instance. The dashed line is the explanation determined, but only locally (Ribeiro, 2016) . Prior Research Bidirectional LSTM Networks have been effectively utilized for fake news detection. Islam et al. (2022) developed a Bidirectional LSTM model capable of classifying news articles into False, Partially False, and True categories. Bahad et al. (2019) conducted a comparative study, evaluating Bidirectional LSTM against other models for binary fake news classification. One relevant paper for our project is Hamed et al. (2023), the model incorporates sentiment analysis by analyzing comments, employing Bidirectional LSTM, and utilizing sentiment analysis with the TextBlob library. The model achieved notable performance with 96.89% accuracy and 97.81% F1 score. The model combined the output of the Bi-LSTM and the sentiment analysis via concatenation. This combined output was then fed through a sigmoid function to obtain the final prediction (Hamed et al., 2023). We hope to draw inspiration from this paper and incorporate the concatenation method into our model. Methods Various machine learning models including logistic regression, decision trees, gradient boosting, and random forest were utilized. Techniques like stratified k-fold cross-validation and grid search have been implemented for hyperparameter tuning, particularly with random forest

11

36