M.S. AAI Capstone Chronicles 2024

Detecting Fake News Using Natural Language Processing graph-based models offer effective solutions, as seen in applications targeting celebrity gossip and healthcare misinformation (Chandra et al., 2020). These methods leverage relationships between articles, sources, and entities to uncover patterns of misinformation propagation. Social network analysis (Sivasankari and Vadivu, 2021) is another valuable approach. This type of analysis examines news article dissemination and user interactions on social platforms to identify suspicious patterns, such as those highlighted by Wasim (2020). The project encompasses three main categories of machine-learning methods. First is traditional algorithms like Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. These are typically utilized for binary classification tasks (Bharadwaj, Shao, 2019). Long Short-Term Memory (LSTM), a type of Recurrent Neural Network (RNN), is employed for analyzing text data, capable of capturing long-term dependencies (Padalko et al., 2024). Lastly, DistilBERT, a lightweight version of the BERT model (Szczepanski et al., 2021), pre-trained on extensive text data, excels in capturing contextual information and semantic relationships. The model has been proven effective for various NLP tasks including text classification. Next is LIME (Sangani, 2021), which we will be using for Model Explainability. Local Interpretable Model-Agnostic Explanations (LIME) is a technique used to interpret the predictions of machine learning models. When coupled with LSTM, it provides explanations for the model's decisions, thereby enhancing transparency and trustworthiness. Our implementation utilizes a Bidirectional Long Short-Term Memory (BiLSTM) network (Padalko et al., 2024), chosen for its effectiveness in various tasks like time-series prediction, natural language processing, and speech recognition. Unlike standard recurrent neural networks (RNNs), LSTMs can look back over 1000 timesteps, thanks to their unique architecture

7

32